└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Awesome Recsys 2 | I share information related to the Recommender Systems that I am interested in. They consist of `RecSys`, `SIGIR`, `KDD`, `WSDM`, `CIKM`, `WWW`, `IJCAI`, `ICML`, `etc`. 3 | 4 | - modified: 2025-09-22 5 | 6 | 7 | 8 | ## Conference Paper 9 | 10 | `SIGIR`, `Recsys`, `WSDM`, `KDD`, `etc`. 11 | 12 | `None` means an unavailable URL or papers that have not been published yet. 13 | 14 | - 2025 15 | - [Recsys 2025](#Recsys-2025) 16 | - [SIGIR 2025](#SIGIR-2025) 17 | - [KDD 2025](#KDD-2025) 18 | - [WSDM 2025](#WSDM-2025) 19 | - [CIKM 2025](#CIKM-2025) 20 | - [WWW 2025](#WWW-2025) 21 | - [IJCAI 2025](#IJCAI-2025) 22 | - [ICML 2025](#ICML-2025) 23 | - [AAAI 2025](#AAAI-2025) 24 | - [NeurIPS'25](#NeurIPS-2025) 25 | 26 | 27 | - 2023 28 | - [Recsys 2023](#Recsys-2023) 29 | - [SIGIR 2023](#SIGIR-2023) 30 | - [KDD 2023](#KDD-2023) 31 | - [WSDM 2023](#WSDM-2023) 32 | - [CIKM 2023](#CIKM-2023) 33 | - [WWW 2023](#WWW-2023) 34 | - [IJCAI 2023](#IJCAI-2023) 35 | - [ICLR 2023](#ICLR-2023) 36 | - [ICML 2023](#ICML-2023) 37 | - [AAAI 2023](#AAAI-2023) 38 | - [NeurIPS'23](#NeurIPS-2023) 39 | 40 | - 2022 41 | - [SIGIR 2022](#SIGIR-2022) 42 | - [RecSys 2022](#RecSys-2022) 43 | - [KDD 2022](#KDD-2022) 44 | - [WSDM 2022](#WSDM-2022) 45 | - [CIKM 2022](#CIKM-2022) 46 | - [WWW 2022](#WWW-2022) 47 | - [IJCAI 2022](#IJCAI-2022) 48 | - [ICML 2022](#ICML-2022) 49 | 50 | 51 | # 2025 52 | 53 | ## Recsys 2025 54 | 55 | - [A Language Model-Based Playlist Generation Recommender System](https://dl.acm.org/doi/pdf/10.1145/3705328.3748053) 56 | - [A Multi-Factor Collaborative Prediction for Review-based Recommendation](https://dl.acm.org/doi/pdf/10.1145/3705328.3748062) 57 | - [A Non-Parametric Choice Model That Learns How Users Choose Between Recommended Options](https://arxiv.org/pdf/2507.20035) 58 | - [Affect-aware Cross-Domain Recommendation for Art Therapy via Music Preference Elicitation](https://arxiv.org/pdf/2507.21120) 59 | - [An Off-Policy Learning Approach for Steering Sentence Generation towards Personalization](https://dl.acm.org/doi/pdf/10.1145/3705328.3748088) 60 | - [Auditing Recommender Systems for User Empowerment in Very Large Online Platforms under the Digital Services Act](https://dl.acm.org/doi/pdf/10.1145/3705328.3748074) 61 | - [Beyond Immediate Click: Engagement-Aware and MoE-Enhanced Transformers for Sequential Movie Recommendation](https://dl.acm.org/doi/pdf/10.1145/3705328.3748076) 62 | - [Breaking Knowledge Boundaries: Cognitive Distillation-enhanced Cross-Behavior Course Recommendation Model](https://dl.acm.org/doi/pdf/10.1145/3705328.3748083) 63 | - [Enhancing Online Video Recommendation via a Coarse-to-fine Dynamic Uplift Modeling Framework](https://dl.acm.org/doi/pdf/10.1145/3705328.3748070) 64 | - [Enhancing Sequential Recommender with Large Language Models for Joint Video and Comment Recommendation](https://dl.acm.org/doi/pdf/10.1145/3705328.3748075) 65 | - [Enhancing Transferability and Consistency in Cross-Domain Recommendations via Supervised Disentanglement](https://arxiv.org/pdf/2507.17112) 66 | - [Exploring Scaling Laws of CTR Model for Online Performance Improvement](https://arxiv.org/pdf/2508.15326) 67 | - [GRACE: Generative Recommendation via Journey-Aware Sparse Attention on Chain-of-Thought Tokenization](https://arxiv.org/pdf/2507.14758?) 68 | - [GenSAR: Unifying Balanced Search and Recommendation with Generative Retrieval](https://dl.acm.org/doi/pdf/10.1145/3705328.3748071) 69 | - [Heterogeneous User Modeling for LLM-based Recommendation](https://arxiv.org/pdf/2507.04626) 70 | - [Hierarchical Graph Information Bottleneck for Multi-Behavior Recommendation](https://arxiv.org/pdf/2507.15395) 71 | - [How Do Users Perceive Recommender Systems’ Objectives?](https://dl.acm.org/doi/pdf/10.1145/3705328.3748066) 72 | - [IP2: Entity-Guided Interest Probing for Personalized News Recommendation](https://arxiv.org/pdf/2507.13622) 73 | - [Integrating Individual and Group Fairness for Recommender Systems through Social Choice](https://dl.acm.org/doi/pdf/10.1145/3705328.3748087) 74 | - [LANCE: Exploration and Reflection for LLM-based Textual Attacks on News Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3705328.3748081) 75 | - [LEAF: Lightweight, Efficient, Adaptive and Flexible Embedding for Large-Scale Recommendation Models](https://dl.acm.org/doi/pdf/10.1145/3705328.3748078) 76 | - [LLM-RecG: A Semantic Bias-Aware Framework for Zero-Shot Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3705328.3748077) 77 | - [LONGER: Scaling Up Long Sequence Modeling in Industrial Recommenders](https://arxiv.org/pdf/2505.04421) 78 | - [Lasso: Large Language Model-based User Simulator for Cross-Domain Recommendation](https://dl.acm.org/doi/pdf/10.1145/3705328.3748048) 79 | - [Leave No One Behind: Fairness-Aware Cross-Domain Recommender Systems for Non-Overlapping Users](https://arxiv.org/pdf/2507.17749) 80 | - [MDSBR: Multimodal Denoising for Session-based Recommendation](https://dl.acm.org/doi/pdf/10.1145/3705328.3748061) 81 | - [Mapping Stakeholder Needs to Multi-Sided Fairness in Candidate Recommendation for Algorithmic Hiring](https://arxiv.org/pdf/2508.00908) 82 | - [Measuring Interaction-Level Unlearning Difficulty for Collaborative Filtering](https://dl.acm.org/doi/pdf/10.1145/3705328.3748092) 83 | - [MoRE: A Mixture of Reflectors Framework for Large Language Model-Based Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3705328.3748055) 84 | - [Modeling Long-term User Behaviors with Diffusion-driven Multi-interest Network for CTR Prediction](https://arxiv.org/pdf/2508.15311) 85 | - [Multi-Granularity Distribution Modeling for Video Watch Time Prediction via Exponential-Gaussian Mixture Network](https://arxiv.org/pdf/2508.12665) 86 | - [NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation](https://arxiv.org/pdf/2507.07522) 87 | - [Non-parametric Graph Convolution for Re-ranking in Recommendation Systems](https://arxiv.org/pdf/2507.09969) 88 | - [Off-Policy Evaluation and Learning for Matching Markets](https://arxiv.org/pdf/2507.13608?) 89 | - [Off-Policy Evaluation of Candidate Generators in Two-Stage Recommender Systems](https://assets.amazon.science/c4/f9/66932e364785bdf271d0fb867408/scipub-approval152129-37504163-offpolicy-evaluation-of-candidate-generators-in-twostage-recommender-systems.pdf) 90 | - [On the Reliability of Sampling Strategies in Offline Recommender Evaluation](https://arxiv.org/pdf/2508.05398?) 91 | - [Paragon: Parameter Generation for Controllable Multi-Task Recommendation](https://dl.acm.org/doi/pdf/10.1145/3705328.3748069) 92 | - [PinFM: Foundation Model for User Activity Sequences at a Billion-scale Visual Discovery Platform](https://arxiv.org/pdf/2507.12704?) 93 | - [Privacy-Preserving Social Recommendation: Privacy Leakage and Countermeasure](https://dl.acm.org/doi/pdf/10.1145/3705328.3748051) 94 | - [Prompt-to-Slate: Diffusion Models for Prompt-Conditioned Slate Generation](https://dl.acm.org/doi/pdf/10.1145/3705328.3748072) 95 | - [RecPS: Privacy Risk Scoring for Recommender Systems](https://arxiv.org/pdf/2507.18365) 96 | - [Recommendation and Temptation](https://arxiv.org/pdf/2412.10595) 97 | - [Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learning](https://arxiv.org/pdf/2503.24289) 98 | - [Scalable Data Debugging for Neighborhood-based Recommendation with Data Shapley Values](https://dl.acm.org/doi/pdf/10.1145/3705328.3748049) 99 | - [Tag-augmented Dual-target Cross-domain Recommendation](https://dl.acm.org/doi/pdf/10.1145/3705328.3748067) 100 | - [Test-Time Alignment with State Space Model for Tracking User Interest Shifts in Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3705328.3748060) 101 | - [USB-Rec: An Effective Framework for Improving Conversational Recommendation Capability of Large Language Mode](https://dl.acm.org/doi/pdf/10.1145/3705328.3748089) 102 | - [VL-CLIP: Enhancing Multimodal Recommendations via Visual Grounding and LLM-Augmented CLIP Embeddings](https://arxiv.org/pdf/2507.17080?) 103 | - [You Don’t Bring Me Flowers: Mitigating Unwanted Recommendations Through Conformal Risk Control](https://arxiv.org/pdf/2507.16829) 104 | - [A Multistakeholder Approach to Value-Driven Co-Design of Recommender Systems Evaluation Metrics in Digital Archives](https://arxiv.org/pdf/2507.03556) 105 | - [‘Beyond the past’: Leveraging Audio and Human Memory for Sequential Music Recommendation](https://arxiv.org/pdf/2507.17356) 106 | - [Beyond Top-1: Addressing Inconsistencies in Evaluating Counterfactual Explanations for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3705328.3748028) 107 | - [Beyond Visit Trajectories: Enhancing POI Recommendation via LLM-Augmented Text and Image Representations](https://dl.acm.org/doi/pdf/10.1145/3705328.3748014) 108 | - [Biases in LLM-Generated Musical Taste Profiles for Recommendation](https://arxiv.org/pdf/2507.16708) 109 | - [Collaborative Interest Modeling in Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3705328.3748023) 110 | - [Consistent Explainers or Unreliable Narrators? Understanding LLM-generated Group Recommendations](https://arxiv.org/pdf/2507.13705?) 111 | - [Correcting the LogQ Correction: Revisiting Sampled Softmax for Large-Scale Retrieval](https://arxiv.org/pdf/2507.09331) 112 | - [Counterfactual Inference under Thompson Sampling](https://arxiv.org/pdf/2504.08773?) 113 | - [D-RDW: Diversity-Driven Random Walks for News Recommender Systems](https://arxiv.org/pdf/2508.13035) 114 | - [Determinants of Users’ Chance-Seeking Behavior in Search-Based Recommendation](https://dl.acm.org/doi/pdf/10.1145/3705328.3748019) 115 | - [Disentangling User and Item Sequence Patterns in Sequential Recommendation Data Sets](https://dl.acm.org/doi/pdf/10.1145/3705328.3748042) 116 | - [Do We Really Need Specialization? Evaluating Generalist Text Embeddings for Zero-Shot Recommendation and Search](https://arxiv.org/pdf/2507.05006) 117 | - [Emotion Vector-Based Fine-Tuning of Large Language Models for Age-Aware Teenage Book Recommendations](https://dl.acm.org/doi/pdf/10.1145/3705328.3748037) 118 | - [Estimating Quantum Execution Requirements for Feature Selection in Recommender Systems Using Extreme Value Theory](https://arxiv.org/pdf/2507.03229) 119 | - [Exploring the Effect of Context-Awareness and Popularity Calibration on Popularity Bias in POI Recommendations](https://arxiv.org/pdf/2507.03503) 120 | - [Failure Prediction in Conversational Recommendation Systems](https://arxiv.org/pdf/2507.17976) 121 | - [Feedback-Driven Gradual Discovery for Expanding Musical Preferences](https://dl.acm.org/doi/pdf/10.1145/3705328.3748025) 122 | - [HiDePCC: A Novel Dual-Pronged Untargeted Attack on Federated Recommendation via Gradient Perturbation and Cluster Crafting](https://dl.acm.org/doi/pdf/10.1145/3705328.3748041) 123 | - [Just Ask for Music (JAM): Multimodal and Personalized Natural Language Music Recommendation](https://arxiv.org/pdf/2507.15826) 124 | - [Large Scale E-Commerce Model for Learning and Analyzing Long-Term User Preferences](https://dl.acm.org/doi/pdf/10.1145/3705328.3748027) 125 | - [Let It Go? Not Quite: Addressing Item Cold Start in Sequential Recommendations with Content-Based Initialization](https://arxiv.org/pdf/2507.19473) 126 | - [Mitigating Latent User Biases in Pre-trained VAE Recommendation Models via On-demand Input Space Transformation](https://dl.acm.org/doi/pdf/10.1145/3705328.3748012) 127 | - [Not Just What, But When: Integrating Irregular Intervals to LLM for Sequential Recommendation](https://arxiv.org/pdf/2507.23209?) 128 | - [Not One News Recommender To Fit Them All: How Different Recommender Strategies Serve Various User Segments]() 129 | - [On Inherited Popularity Bias in Cold-Start Item Recommendation](https://cris.vub.be/ws/portalfiles/portal/134233847/recsys25-40_4_.pdf) 130 | - [Personalized Persuasion-Aware Explanations in Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3705328.3748021) 131 | - [Popularity-Bias Vulnerability: Semi-Supervised Label Inference Attack on Federated Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3705328.3748024) 132 | - [Rethinking Overconfidence in VAEs: Can Label Smoothing Help?](https://dl.acm.org/doi/pdf/10.1145/3705328.3748039) 133 | - [SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation](https://arxiv.org/pdf/2507.13336) 134 | - [Stairway to Fairness: Connecting Group and Individual Fairness](https://arxiv.org/pdf/2508.21334) 135 | - [Towards Personality-Aware Explanations for Music Recommendations Using Generative AI](https://dl.acm.org/doi/pdf/10.1145/3705328.3748032) 136 | - [TreatRAG: A Framework for Personalized Treatment Recommendation](https://dl.acm.org/doi/pdf/10.1145/3705328.3748022) 137 | - [A Media Content Recommendation Method for Playlist Curators using LLM-Based Query Expansion](https://dl.acm.org/doi/pdf/10.1145/3705328.3748129) 138 | - [Agentic Personalisation of Cross-Channel Marketing Experiences](https://arxiv.org/pdf/2506.16429?) 139 | - [An Analysis of Learned Product Embeddings in an E-Commerce Context](https://dl.acm.org/doi/pdf/10.1145/3705328.3748131) 140 | - [Balanced Public Service Media Recommendation Trade-offs with a Light Carbon Footprint](https://dl.acm.org/doi/pdf/10.1145/3705328.3748106) 141 | - [Balancing Fine-tuning and RAG: A Hybrid Strategy for Dynamic LLM Recommendation Updates](https://dl.acm.org/doi/pdf/10.1145/3705328.3748105) 142 | - [Closing the Online-Offline Gap: A Scalable Framework for Composed Model Evaluation](https://dl.acm.org/doi/pdf/10.1145/3705328.3748117) 143 | - [Cold Starting a New Content Type: A Case Study with Netflix Live](https://dl.acm.org/doi/pdf/10.1145/3705328.3748112) 144 | - [Contrastive Conditional Embeddings for Item-based Recommendation at E-commerce Scale](https://dl.acm.org/doi/pdf/10.1145/3705328.3748095) 145 | - [Cross-Batch Aggregation for Streaming Learning from Label Proportions in Industrial-Scale Recommendation Systems](https://dl.acm.org/doi/pdf/10.1145/3705328.3748115) 146 | - [Decoupled Entity Representation Learning for Pinterest Ads Ranking](https://arxiv.org/pdf/2509.04337) 147 | - [Deep Reinforcement Learning for Ranking Utility Tuning in the Ad Recommender System at Pinterest](https://dl.acm.org/doi/pdf/10.1145/3705328.3748144) 148 | - [Efficient Off-Policy Evaluation of Content Blending in Station-Based Music Experiences](https://assets.amazon.science/4d/fe/122046754139897f436f3974e969/scipub-approval152129-39223260-efficient-offpolicy-evaluation-of-content-blending-in-stationbased-music-experiences.pdf) 149 | - [Enhancing Embedding Representation Stability in Recommendation Systems with Semantic ID](https://arxiv.org/pdf/2504.02137?) 150 | - [Enhancing Online Ranking Systems via Multi-Surface Co-Training for Content Understanding](https://dl.acm.org/doi/pdf/10.1145/3705328.3748101) 151 | - [Generalized User Representations for Large-Scale Recommendations and Downstream Tasks](https://dl.acm.org/doi/pdf/10.1145/3705328.3748132) 152 | - [Identifying Offline Metrics that Predict Online Impact: A Pragmatic Strategy for Real-World Recommender Systems](https://arxiv.org/pdf/2507.09566) 153 | - [Improve the Personalization of Large-Scale Ranking Systems by Integrating User Survey Feedback](https://dl.acm.org/doi/pdf/10.1145/3705328.3748119) 154 | - [Improving Visual Recommendation on E-commerce Platforms Using Vision-Language Models](https://dl.acm.org/doi/pdf/10.1145/3705328.3748128) 155 | - [In-context Learning for Addressing User Cold-start in Sequential Movie Recommenders](https://dl.acm.org/doi/pdf/10.1145/3705328.3748109) 156 | - [Industry Insights from Comparing Deep Learning and GBDT Models for E-Commerce Learning-to-Rank](https://arxiv.org/pdf/2507.20753) 157 | - [Item-centric Exploration for Cold Start Problem](https://arxiv.org/pdf/2507.09423) 158 | - [Kamae: Bridging Spark and Keras for Seamless ML Preprocessing](https://arxiv.org/pdf/2507.06021?) 159 | - [LADDER: LLM-Annotated Data for Dogfooded Evaluation of Rankings](https://dl.acm.org/doi/pdf/10.1145/3705328.3748094) 160 | - [LLM-Powered Nuanced Video Attribute Annotation for Enhanced Recommendations](https://dl.acm.org/doi/pdf/10.1145/3705328.3748103) 161 | - [Leveraging Explicit Negative Feedback in Large-Scale Recommendation Systems: A Case Study](https://dl.acm.org/doi/pdf/10.1145/3705328.3748145) 162 | - [Location Matters: Leveraging Multi-Resolution Geo-Embeddings for Housing Search](https://dl.acm.org/doi/pdf/10.1145/3705328.3748136) 163 | - [Metadata Generation and Evaluation using LLMs – Case Study on Canonical Titles](https://dl.acm.org/doi/pdf/10.1145/3705328.3748100) 164 | - [Minimize Negative Experiences in Video Recommendation Systems with Multimodal Large Language Models](https://dl.acm.org/doi/pdf/10.1145/3705328.3748102) 165 | - [Never Miss an Episode: How LLMs are Powering Serial Content Discovery on YouTube](https://dl.acm.org/doi/pdf/10.1145/3705328.3748104) 166 | - [Not All Impressions Are Created Equal: Psychology-Informed Retention Optimization for Short-Form Video Recommendation](https://dl.acm.org/doi/pdf/10.1145/3705328.3748122) 167 | - [Operational Twin–Driven AI Recommender for Strategic Service Planning](https://dl.acm.org/doi/pdf/10.1145/3705328.3748099) 168 | - [Orthogonal Low Rank Embedding Stabilization](https://arxiv.org/pdf/2508.07574) 169 | - [Pareto-Optimal Solution: Optimizing Engagement and Revenue](https://dl.acm.org/doi/pdf/10.1145/3705328.3748142) 170 | - [Personalized Interest Graphs for Theme-Driven User Behavior](https://dl.acm.org/doi/pdf/10.1145/3705328.3748133) 171 | - [Practical Multi-Task Learning for Rare Conversions in Ad Tech](https://arxiv.org/pdf/2507.20161) 172 | - [RADAR: Recall Augmentation through Deferred Asynchronous Retrieval](https://arxiv.org/pdf/2506.07261) 173 | - [RankGraph: Unified Heterogeneous Graph Learning for Cross-Domain Recommendation](https://arxiv.org/pdf/2509.02942) 174 | - [SASRec in Action: Real-World Adaptations for ZDF Streaming Service](https://dl.acm.org/doi/pdf/10.1145/3705328.3748097) 175 | - [SEMORec: A Scalarized Efficient Multi-Objective Recommendation Framework](https://dl.acm.org/doi/pdf/10.1145/3705328.3748140) 176 | - [Scaling Generative Recommendations with Context Parallelism on Hierarchical Sequential Transducers](https://arxiv.org/pdf/2508.04711?) 177 | - [Scaling Image Variant Optimization Through Customer Bucketing and Response Caching: A Large-Scale Implementation at Amazon Prime Video](https://dl.acm.org/doi/pdf/10.1145/3705328.3748134) 178 | - [Scaling Retrieval for Web-Scale Recommenders: Lessons from Inverted Indexes to Embedding Search](https://dl.acm.org/doi/pdf/10.1145/3705328.3748116) 179 | - [Semantic IDs for Music Recommendation](https://arxiv.org/pdf/2507.18800) 180 | - [Simulating Discoverability for Upcoming Content in TV Entertainment Platforms](https://dl.acm.org/doi/pdf/10.1145/3705328.3748137) 181 | - [SocRipple: A Two-Stage Framework for Cold-Start Video Recommendations](https://arxiv.org/pdf/2508.07241) 182 | - [Stream Normalization for CTR Prediction](https://dl.acm.org/doi/pdf/10.1145/3705328.3748093) 183 | - [Streaming Trends: A Low-Latency Platform for Dynamic Video Grouping and Trending Corpora Building](https://dl.acm.org/doi/pdf/10.1145/3705328.3748120) 184 | - [Suggest, Complement, Inspire: Story of Two-Tower Recommendations at Allegro.com](https://arxiv.org/pdf/2508.03702?) 185 | - [The Future is Sparse: Embedding Compression for Scalable Retrieval in Recommender Systems](https://arxiv.org/pdf/2505.11388) 186 | - [USD: A User-Intent-Driven Sampling and Dual-Debiasing Framework for Large-Scale Homepage Recommendations](https://arxiv.org/pdf/2507.06503) 187 | - [Unified Survey Modeling to Limit Negative User Experiences in Recommendation Systems](https://dl.acm.org/doi/pdf/10.1145/3705328.3748108) 188 | - [User Long-Term Multi-Interest Retrieval Model for Recommendation](https://arxiv.org/pdf/2507.10097) 189 | - [You Say Search, I Say Recs: A Scalable Agentic Approach to Query Understanding and Exploratory Search at Spotify](https://dl.acm.org/doi/pdf/10.1145/3705328.3748127) 190 | - [Zero-shot Cross-domain Knowledge Distillation: A Case study on YouTube Music](https://dl.acm.org/doi/pdf/10.1145/3705328.3748138) 191 | 192 | 193 | ## SIGIR 2025 194 | 195 | - [Beyond Whole Dialogue Modeling: Contextual Disentanglement for Conversational Recommendation](https://dl.acm.org/doi/pdf/10.1145/3726302.3729903) 196 | - [Agentic Feedback Loop Modeling Improves Recommendation and User Simulation](https://dl.acm.org/doi/pdf/10.1145/3726302.3729893?casa_token=w6eW3jbDgakAAAAA:Uwcu0mfYL5vYbIEdGYh97lkB8aCmCIDwsrte9ZLzsGZ2YzB_0pYPQxa6PubYbHhBXp01rjhKo18Wgdg) 197 | - [Unleashing the Potential of Diffusion Models Towards Diversified Sequential Recommendations](https://dl.acm.org/doi/pdf/10.1145/3726302.3730109) 198 | - [Pre-training for Unlearning: A Model-agnostic Paradigm for Recommendation Unlearning](https://dl.acm.org/doi/pdf/10.1145/3726302.3730060?casa_token=QWuR9VZ-CikAAAAA:W5CD6LvTK5tUv7DPfAiskEcxYw7W2evIAsI-r0YhfkM4aHt9vdXRJcN_jNpr54BohJJUAfwqllILxUA) 199 | - [Hierarchical Intent-guided Optimization with Pluggable LLM-Driven Semantics for Session-based Recommendation](https://dl.acm.org/doi/pdf/10.1145/3726302.3729994?casa_token=RbxQns48X_MAAAAA:3QLWSx2htdU-Q98EQ0T2-5TE__M6slYv73XHn1_CO2kZJ5bue0SzJP8yF1Zudqnj7Schf_Zwhc56g0c) 200 | - [VoRec: Enhancing Recommendation with Voronoi Diagram in Hyperbolic Space](https://dl.acm.org/doi/pdf/10.1145/3726302.3730114?casa_token=HK_8q8-eNmMAAAAA:PNPgCZPZZ7FjWBfXZwXVhtZXNE5exIvxaadYm3DgpmMky2jfJ2xEqL_UgT8IAFWqWM2U1HcfqOnYMWQ) 201 | - [Collaborative Signal-guided Diffusion Models for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3726302.3729929?casa_token=ssfI1QsklxEAAAAA:Kow-bXDB5Ni_k4TYtr7tlJfq6734oPQAYhroxmxcL7oLhRU8d3sqs4_uXI0jn2J_FREaHCpc6WOUm58) 202 | - [CORONA: A Coarse-to-Fine Framework for Graph-based Recommendation with Large Language Models](https://dl.acm.org/doi/pdf/10.1145/3726302.3729937?casa_token=tX6F1MaTmCgAAAAA:k35i7brdd7qdnigB1LHFRdNe2fyit7kur7ib0Hkht7QbkXh8ZbYTR7F4zrzksolcnMhwLBsubUMHL5U) 203 | - [NR4DER: Neural Re-ranking for Diversified Exercise Recommendation](https://dl.acm.org/doi/pdf/10.1145/3726302.3730046?casa_token=N7oJRw3FFaoAAAAA:aEtTaWDyvxEP1Ep2A_bJPMsqdsEn65yyQdpfEsKdURDPFTlMO69iO2SICemBMfjViANWWITHXi6t1SI) 204 | - [Large Language Models Enhanced Hyperbolic Space Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3726302.3730019?casa_token=wPcxOC8uQRYAAAAA:J3duUBxvGCZjmhFDeIIkAWUWrGj7JZvQp7dSEZ3AbuesDRp_RrZQ_LcTrak-alQ_K1HyVZu9-_SLUpM) 205 | - [Linear Item-Item Models with Neural Knowledge for Session-based Recommendation](https://dl.acm.org/doi/pdf/10.1145/3726302.3730024) 206 | - 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[Killing Two Birds with One Stone: Unifying Retrieval and Ranking with a Single Generative Recommendation Model](https://dl.acm.org/doi/pdf/10.1145/3726302.3730017?casa_token=ns3L_r-u-zUAAAAA:d2PsU4U8DsXVeLSdYXl22nTO-CqmpD0KmiXX1XypMmqPsgSbjeMX5GI5X11kpoP2z2iuekyvgFSrSfE) 265 | - [Adaptive Graph Integration for Cross-Domain Recommendation via Heterogeneous Graph Coordinators](https://dl.acm.org/doi/pdf/10.1145/3726302.3729886) 266 | - [DARLR: Dual-Agent Offline Reinforcement Learning for Recommender Systems with Dynamic Reward](https://dl.acm.org/doi/pdf/10.1145/3726302.3729942) 267 | - [Fair Recommendation with Biased-Limited Sensitive Attribute](https://dl.acm.org/doi/pdf/10.1145/3726302.3729974?casa_token=V72GiUchRKgAAAAA:eiViTr5wwvsLABb0SQekL3hNh8OXfKHxQKCXdqRIsYTLnhAvy7SFqZBaFWKPvNmGy41_lGJ4dwL5o_I) 268 | - [Diversity-aware Dual-promotion Poisoning Attack on Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3726302.3729955) 269 | - [Improving Sequential Recommenders through Counterfactual Augmentation of System Exposure](https://dl.acm.org/doi/pdf/10.1145/3726302.3730005) 270 | - [Social Relation-Level Privacy Risks and Preservation in Social Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3726302.3730086?casa_token=mPPxx3eyRjUAAAAA:CFklDs9TYxzDUofCwZf8A_2OVAVWAVu0Jhj0bAR6eVHjZbiFDdoZPLaOiYMZTWcouI3w9N3McMlaZz8) 271 | - [Balancing Self-Presentation and Self-Hiding for Exposure-Aware Recommendation Based on Graph Contrastive Learning](https://dl.acm.org/doi/pdf/10.1145/3726302.3729900) 272 | - [Disentangled Graph Debiasing for Next POI Recommendation](https://dl.acm.org/doi/pdf/10.1145/3726302.3729952?casa_token=EuUVvRfrB10AAAAA:klj1OzRbC-uqqV8a7RFZu3GRCDaF_T7oU7vP1exoOQflub4t6NDVRDsqtgXKxxIWSIHnQQqTt9rs32Y) 273 | - [Exploring the Escalation of Source Bias in User, Data, and Recommender System Feedback Loop](https://dl.acm.org/doi/pdf/10.1145/3726302.3729972?casa_token=VWaseleac4EAAAAA:DQMSdoQ7pkw2Tl5vg_SPI7dDXz3rH-BnXJD6nSEz4Md7gxLOKVa2RnqWEWAC4cWWf0DWXwCqS-7EeFI) 274 | - [Joint Item Embedding Dual-view Exploration and Adaptive Local-Global Fusion for Federated Recommendation](https://dl.acm.org/doi/pdf/10.1145/3726302.3730016?casa_token=x8vYLne3BagAAAAA:Z1_ZB6leGF8wELtYRLx7YY8ZpRooRk-4O7qHq0crsUk7hprPZg5ijKGsciAsh53zr_RHNxlyWgBn9us) 275 | - [Adaptive user Dynamic Interest Guidance for Generative Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3726302.3729888?casa_token=Tl6A7okAojMAAAAA:D2JSTQHJ-b1s0V33a6H6m02nIiKHoHmLOc9VNfiFUxJR0WiQnSgk04YmWc1xK7yQmrPTX_J2ixpx--U) 276 | - [Refining Fidelity Metrics for Explainable Recommendations](https://dl.acm.org/doi/pdf/10.1145/3726302.3730242) 277 | - [Interest Changes: Considering User Interest Life Cycle in Recommendation System](https://dl.acm.org/doi/pdf/10.1145/3726302.3730215?casa_token=5T9ckXuJDgkAAAAA:-8XA2IS7D_YjIhTHXgrWd6ajwM3cjEtqGx8MZZSBK08VXeQIxup7fD01WwUL0uNnILM7TpLqbURnDdw) 278 | - [HeterRec: Heterogeneous Information Transformer for Scalable Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3726302.3730206?casa_token=S7h0RxoQjmEAAAAA:pGbTcrnhF9PJhvfy7SrleOZTeLErBQpyPQ7VTi2ET2UgNqKpGPlDOfmOoPH4K7GstgR3I7eZs6dNoTI) 279 | - [Training-free Periodic Interest Augmentation in Incremental Recommendation](https://dl.acm.org/doi/pdf/10.1145/3726302.3730258?casa_token=Dtk2cCM-susAAAAA:-Ru3eD8DLRoCLenjZpQ75-e9yQVepgFyJbFQ3tGhroqf0WZ34WFTPRNOQohs042c4huEftbAbBLpxOQ) 280 | - [GEAR: Generalized Alternating Regressor for Multi-Behavior Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3726302.3730200?casa_token=Qh63_C0E6FgAAAAA:9FmPj9VPDtn43E9wYVdfFwsO7aZBzmhgEPVGa4-o8I4olEerSs_sx-O1ByajWoUVr2s787y8pi6Rt_E) 281 | - [SEALR: Sequential Emotion-Aware LLM-Based Personalized Recommendation System](https://dl.acm.org/doi/pdf/10.1145/3726302.3730249) 282 | - [Towards Principled Learning for Re-ranking in Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3726302.3730257?casa_token=JRouGtg-F_oAAAAA:BfE_BDTtfD9Zz-_NH4SY0RT5hd2letDyErQmKFE9AxGHW2nSKTxREwXQiGIp78cVqmtH79Uc7bjwiUg) 283 | - [SMMR: Sampling-Based MMR Reranking for Faster, More Diverse, and Balanced Recommendations and Retrieval](https://dl.acm.org/doi/pdf/10.1145/3726302.3730250) 284 | - [Multi-Interest Matching for Personalized News Recommendation with Large Language Models](https://dl.acm.org/doi/pdf/10.1145/3726302.3730233?casa_token=igWzi9ERQgcAAAAA:4Eji0ErXE5AKSQcnYiSp6NejoD8vrBq1ZedW9bFgpCklQBENcCzqgvTab5X-SQlAp23pP__VkmdBI5k) 285 | - [AgentCF++: Memory-enhanced LLM-based Agents for Popularity-aware Cross-domain Recommendations](https://dl.acm.org/doi/pdf/10.1145/3726302.3730161?casa_token=HfBmbMEpsN4AAAAA:fAqdYpJWodpmvBKt4W8wAhwUhz2B49-qZX_FbmGqV8As2s2kavPd3FcvyShKTqVZMVXEKmWVtYZ7DFw) 286 | - [Improving LLM-powered Recommendations with Personalized Information](https://dl.acm.org/doi/pdf/10.1145/3726302.3730211?casa_token=T7hsCVVocl4AAAAA:0H1IZewaRlK0oswnNkTAECOiXAR_LWpBxrq0MKJTRPw85FvvPvejsJyeyyWnw_PeUjtiw2EIiOE7lT8) 287 | - [Dual Debiasing in LLM-based Recommendation](https://dl.acm.org/doi/pdf/10.1145/3726302.3730181?casa_token=Jw9PqjpOu1QAAAAA:9v8T_0mGlPHIs1w0wa7SZJQ3s_IWxX0AdzJ6hNv_hqKmRZLC4ia7R2s0dfy3CwJPMf55fzxhhRLjbXs) 288 | - [PUB: An LLM-Enhanced Personality-Driven User Behaviour Simulator for Recommender System Evaluation](https://dl.acm.org/doi/pdf/10.1145/3726302.3730238) 289 | - [Do LLMs Memorize Recommendation Datasets? A Preliminary Study on MovieLens-1M](https://dl.acm.org/doi/pdf/10.1145/3726302.3730178) 290 | - [LLM as User Simulator: Towards Training News Recommender without Real User Interactions](https://dl.acm.org/doi/pdf/10.1145/3726302.3730224?casa_token=lupupFjt084AAAAA:V7bMJhBVbUc13zqL2RxFX5O7KieIa5qW3VCoABHYKXy6J6qSab3AiChDPbdKP9YYbsLwylYIKRw5gD8) 291 | - [Private Preferences, Public Rankings: A Privacy-Preserving Framework for Marketplace Recommendations](https://dl.acm.org/doi/pdf/10.1145/3726302.3730237) 292 | - [Translative Neural Team Recommendation: From Multilabel Classification to Sequence Prediction](https://dl.acm.org/doi/pdf/10.1145/3726302.3730259) 293 | - [From Monolith to Mosaic: Uncovering Behavioral Differences for Choice Models in Recommender Systems Simulations](https://dl.acm.org/doi/pdf/10.1145/3726302.3730199) 294 | - [FROG: Effective Friend Recommendation in Online Games via Modality-aware User Preferences](https://dl.acm.org/doi/pdf/10.1145/3726302.3730198) 295 | - [CoSRec: A Joint Conversational Search and Recommendation Dataset](https://dl.acm.org/doi/pdf/10.1145/3726302.3730319) 296 | - [SynthTRIPs: A Knowledge-Grounded Framework for Benchmark Data Generation for Personalized Tourism Recommenders](https://dl.acm.org/doi/pdf/10.1145/3726302.3730321) 297 | - [Benchmarking Recommendation, Classification, and Tracing Based on Hugging Face Knowledge Graph](https://dl.acm.org/doi/pdf/10.1145/3726302.3730277?casa_token=7ZIX-KdK4ZwAAAAA:X2SsKND9VLrAeAOO8xt8KDaB24tTc8greKMpopSYbpT-ZAAVUhWj_rRDsQESFmn0Sr1jQhnFE0fr66o) 298 | - [DataRec: A Python Library for Standardized and Reproducible Data Management in Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3726302.3730320) 299 | - [Reassessing the Effectiveness of Reinforcement Learning based Recommender Systems for Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3726302.3730322) 300 | - [A Worrying Reproducibility Study of Intent-Aware Recommendation Models](https://dl.acm.org/doi/pdf/10.1145/3726302.3730307) 301 | - [FairWork: A Generic Framework For Evaluating Fairness In LLM-Based Job Recommender System](https://dl.acm.org/doi/pdf/10.1145/3726302.3730145?casa_token=OfXSA4jOu6oAAAAA:mtMnVbEvDhfIVvF892b5Ca5cBCPHT1wk8br2bS-anMO0ZNqv-Cm7opprW6mJLL0s2KiBEWCOAobEUVs) 302 | - [NodeRec+: A Lightweight Framework for Federated Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3726302.3730138?casa_token=gHauhB5Wu1kAAAAA:TUIdhWXc5q_Ird9zRASqlsIo-6Jm6dWYAUfiQTkLR5KJz2ErYpQsFAAA0cs9ZW-i6qwsZtSCLdUibxA) 303 | - [InstInfo: A Just-in-Time Literature Recommendation System for Presentations](https://dl.acm.org/doi/pdf/10.1145/3726302.3730146?casa_token=dfHMZtZ1y6AAAAAA:WOY1CwPMcwdtjnrA9CUpEwtVtd28LBEbp7PHa1bufHGQsxHHp1aLYP295UOkLnuKZvjWufkSyZc8kyk) 304 | - [Toward Holistic Evaluation of Recommender Systems Powered by Generative Models](https://dl.acm.org/doi/pdf/10.1145/3726302.3730354) 305 | - [Small Data, Big Impact: Navigating Resource Limitations in Point-of-Interest Recommendation for Individuals with Autism](https://dl.acm.org/doi/pdf/10.1145/3726302.3730269?casa_token=fM-G-b1rZaYAAAAA:nPZ2ROXqgEKHU4F8JBeIs9qFEdcaNavYsY2RU5JvOYAVA8codl7myC5DS2flhPHBR_-NbW2c03nW958) 306 | - [When Less is Enough: Optimizations for Low-Cost Recommendation Systems](https://dl.acm.org/doi/pdf/10.1145/3726302.3730267?casa_token=m9voPT9EH_YAAAAA:V0Sw35nB4CymRHz2vNKgrVXtTjrObNRGU6sqrX1oB5OKhtzlCWTgoSES4i701JUfU1RUWDQ5a9y5FKg) 307 | - [Adaptive Domain Scaling for Personalized Sequential Modeling in Recommenders](https://dl.acm.org/doi/pdf/10.1145/3726302.3731939?casa_token=GvQ0fN2C8s8AAAAA:dGJ8ZcNkcK4fBurLQHNgrPmpZB39kbijE1qEOZ0_b7zFjH3oUTLdMgx9BB8GvIM9NqiFwqetORKX7FE) 308 | - [Pyramid Mixer: Multi-dimensional Multi-period Interest Modeling for Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3726302.3731968?casa_token=XpgDhXBS9iYAAAAA:loqCn7t7pl6SO7tBksf6dwiPqhYRoz9j9IWdAmoACrR-Zr08xGGfLhhWrJ0bKy45dTCyR7mXjOfL1q4) 309 | - [SuperRS: Multi Scenario Reciprocal-Aware Dual MoE for Unified Recommendation-Search Ranking](https://dl.acm.org/doi/pdf/10.1145/3726302.3731949?casa_token=INjJOCfMIz8AAAAA:FYkB_g3KLYLUyRpO78QRNM4JqsKS0q-OKcaUWH2LOoLZvF3x8KjFsYhkWeRuptU7NsypmHnWFeB_wno) 310 | - [Federated Recommender System Based on Diffusion Augmentation and Guided Denoising](https://dl.acm.org/doi/pdf/10.1145/3688570?casa_token=6MSmukW2_n0AAAAA:u2cbAqvUOszYtjIl1UZ5L3aO2BpIkY-dPMWjb7_pqENzlDCesNB8Hken1Mw9ddDZVR2BXniB6MX170I) 311 | - [Explaining Recommendation Fairness from a User/Item Perspective](https://dl.acm.org/doi/pdf/10.1145/3698877?casa_token=3RwqHtOzxr0AAAAA:B2jhHln3uLPf-6nGobtsy3pYRyVhBACp1-WNTMDtMHsTk5EuEwOMXM3IQjgAS0QNE5VRSAQynM1GBak) 312 | - [CAFE+: Towards Compact, Adaptive, and Fast Embedding for Large-scale Online Recommendation Models](https://dl.acm.org/doi/pdf/10.1145/3713072?casa_token=aRUB2FdDL-EAAAAA:lYt5UaFonhxE83KIqRiyJg6p5fG7L9vlGE_oMXCp-U2Pj-1mC2PjRhoksrsracWO0gZ6qoE2IZBogek) 313 | - [Automated Disentangled Sequential Recommendation with Large Language Models](https://dl.acm.org/doi/pdf/10.1145/3675164) 314 | - [LTP-MMF: Towards Long-Term Provider Max-Min Fairness Under Recommendation Feedback Loops](https://dl.acm.org/doi/pdf/10.1145/3695867?casa_token=aRb3oDFa318AAAAA:Bn8BZk5OSldyO61bSHnlRBOK0YfZH41kjmIONZSbRGCaXGie6-1NwUEym6ihDIjZyGb3VOF1j7-gG_s) 315 | - [Feature-Enhanced Neural Collaborative Reasoning for Explainable Recommendation](https://dl.acm.org/doi/pdf/10.1145/3690381) 316 | - [Knowledge-Enhanced Conversational Recommendation via Transformer-Based Sequential Modeling](https://dl.acm.org/doi/pdf/10.1145/3677376?casa_token=duy_BBkb55UAAAAA:vZ5ubmKG9IEVoSHFcolFbf91oVYagXOmH9p52oily0wfCxyNcYbbZSF7EcmK-f3iIwxpGScgp0OPhsY) 317 | 318 | ## KDD 2025 319 | 320 | - [A Unified Online-Offline Framework for Co-Branding Campaign Recommendations](https://www.cse.cuhk.edu.hk/~cslui/PUBLICATION/KDD-25-Co_Branding_Campaign_Recommendations.pdf) 321 | - [Adapting Large Vision-Language Models to Visually-Aware Conversational Recommendation](https://dl.acm.org/doi/pdf/10.1145/3711896.3736828) 322 | - [Addressing Correlated Latent Exogenous Variables in Debiased Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3711896.3736832) 323 | - [Breaking the Bottleneck: User-Specific Optimization and Real-Time Inference Integration for Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3711896.3736865) 324 | - [Breaking the Top-$K$ Barrier: Advancing Top-$K$ Ranking Metrics Optimization in Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3711896.3736866) 325 | - [Bridging Textual-Collaborative Gap through Semantic Codes for Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3711896.3736869) 326 | - [Contrastive Text-enhanced Transformer for Cross-Domain Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3711896.3736893) 327 | - [Data Watermarking for Sequential Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3711896.3736903) 328 | - [EARN: Efficient Inference Acceleration for LLM-based Generative Recommendation by Register Tokens](https://dl.acm.org/doi/pdf/10.1145/3711896.3736919) 329 | - [FairCDR: Transferring Fairness and User Preferences for Cross-Domain Recommendation](https://dl.acm.org/doi/pdf/10.1145/3711896.3736951) 330 | - [Feature Reconstruction for Anomaly Detection on Directed Multigraphs: A Preprocessing Framework for GNNs](https://dl.acm.org/doi/pdf/10.1145/3711896.3736953) 331 | - [FindRec: Stein-Guided Entropic Flow for Multi-Modal Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3711896.3736968) 332 | - [Generating Long Semantic IDs in Parallel for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3711896.3736979) 333 | - [Generative Next POI Recommendation with Semantic ID](https://dl.acm.org/doi/pdf/10.1145/3711896.3736981) 334 | - [GORACS: Group-level Optimal Transport-guided Coreset Selection for LLM-based Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3711896.3736985) 335 | - [Gradients as An Action: Towards Communication-Efficient Federated Recommender Systems via Adaptive Action Sharing](https://dl.acm.org/doi/pdf/10.1145/3711896.3736987) 336 | - [LettinGo: Explore User Profile Generation for Recommendation System](https://dl.acm.org/doi/pdf/10.1145/3711896.3737024) 337 | - [LightKG: Efficient Knowledge-Aware Recommendations with Simplified GNN Architecture](https://dl.acm.org/doi/pdf/10.1145/3711896.3737026) 338 | - [LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3711896.3737029) 339 | - [Lost in Sequence: Do Large Language Models Understand Sequential Recommendation?](https://dl.acm.org/doi/pdf/10.1145/3711896.3737035) 340 | - [MDVT: Enhancing Multimodal Recommendation with Model-Agnostic Multimodal-Driven Virtual Triplets](https://dl.acm.org/doi/pdf/10.1145/3711896.3737042) 341 | - [Measure Domain’s Gap: A Similar Domain Selection Principle for Multi-Domain Recommendation](https://dl.acm.org/doi/pdf/10.1145/3711896.3737043) 342 | - [Modality-Aware Diffusion Augmentation with Consistent Subspace Disentanglement for Session-based Recommendation](https://dl.acm.org/doi/pdf/10.1145/3711896.3737052) 343 | - [MockLLM: A Multi-Agent Behavior Collaboration Framework for Online Job Seeking and Recruiting](https://dl.acm.org/doi/pdf/10.1145/3711896.3737051) 344 | - [Multi-Grained Preference Enhanced Transformer for Multi-Behavior Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3711896.3737059) 345 | - [One-shot Multi-view Visual Conversational Recommendation](https://dl.acm.org/doi/pdf/10.1145/3711896.3737070) 346 | - [Preference-Optimized Retrieval and Ranking for Efficient Multimodal Recommendation](https://dl.acm.org/doi/pdf/10.1145/3711896.3737088) 347 | - [Revisiting Self-Attention for Cross-Domain Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3711896.3737108) 348 | - [Scaling Transformers for Discriminative Recommendation via Generative Pretraining](https://dl.acm.org/doi/pdf/10.1145/3711896.3737117) 349 | - [Shapley Value-driven Data Pruning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3711896.3737127) 350 | - [SMA-GNN: A Symbol-Aware Graph Neural Network for Signed Link Prediction in Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3711896.3737132) 351 | - [STARLINE: Contrastive Learning with Modality-Aware Graph Refinement for Effective Multimedia Recommendation](https://dl.acm.org/doi/pdf/10.1145/3711896.3737136) 352 | - [Taming Recommendation Bias with Causal Intervention on Evolving Personal Popularity](https://dl.acm.org/doi/pdf/10.1145/3711896.3737146) 353 | - [TarDiff: Target-Oriented Diffusion Guidance for Synthetic Electronic Health Record Time Series Generation](https://dl.acm.org/doi/pdf/10.1145/3711896.3737147) 354 | - [Time Matters: Enhancing Sequential Recommendations with Time-Guided Graph Neural ODEs](https://dl.acm.org/doi/pdf/10.1145/3711896.3737156) 355 | - [Unlocking the Power of Diffusion Models in Sequential Recommendation: A Simple and Effective Approach](https://dl.acm.org/doi/pdf/10.1145/3711896.3737172) 356 | - [Your Graph Recommenders are Provably Doing Graph Contrastive Learning](https://dl.acm.org/doi/pdf/10.1145/3711896.3737182) 357 | 358 | 359 | ## WSDM 2025 360 | - [Review-Based Hyperbolic Cross-Domain Recommendation](https://dl.acm.org/doi/pdf/10.1145/3701551.3703486) 361 | - [Large Language Model driven Policy Exploration for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3701551.3703496) 362 | - [Combating Heterogeneous Model Biases in Recommendations via Boosting](https://dl.acm.org/doi/pdf/10.1145/3701551.3703505) 363 | - [Teach Me How to Denoise: a Universal Framework for Denoising Multi-modal Recommender Systems via Guided Calibration](https://dl.acm.org/doi/pdf/10.1145/3701551.3703507) 364 | - [DDualSE: Decoupled Dual-head Squeeze and Excitation Attention for Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3701551.3703509) 365 | - [Privacy-Preserving Orthogonal Aggregation for Guaranteeing Gender Fairness in Federated Recommendation](https://dl.acm.org/doi/pdf/10.1145/3701551.3703513) 366 | - [SCONE: A Novel Stochastic Sampling to Generate Contrastive Views and Hard Negative Samples for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3701551.3703522) 367 | - [Towards Personalized Federated Multi-Scenario Multi-Task Recommendation](https://dl.acm.org/doi/pdf/10.1145/3701551.3703523) 368 | - [An aspect performance-aware hypergraph neural network for review-based recommendation](https://dl.acm.org/doi/pdf/10.1145/3701551.3703528) 369 | - [A Contrastive Framework with User, Item and Review Alignment for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3701551.3703530) 370 | - [DeMBR: Denoising Model with Memory Pruning and Semantic Guidance for Multi-Behavior Recommendation](https://dl.acm.org/doi/pdf/10.1145/3701551.3703532) 371 | - [Exploration and Exploitation of Hard Negative Samples for Cross-Domain Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3701551.3703535) 372 | - [LightGNN: Simple Graph Neural Networks for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3701551.3703536) 373 | - [AMLCDR: An Adaptive Meta-Learning Model for Cross-Domain Recommendation by Aligning Preference Distributions](https://dl.acm.org/doi/pdf/10.1145/3701551.3703539) 374 | - [Oracle-guided Dynamic User Preference Modeling for Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3701551.3703542) 375 | - [Personalised Outfit Recommendation via History-aware Transformer](https://dl.acm.org/doi/pdf/10.1145/3701551.3703545) 376 | - [Large Language Model Simulator for Cold-Start Recommendation](https://dl.acm.org/doi/pdf/10.1145/3701551.3703546) 377 | - [Facet-Aware Multi-Head Mixture-of-Experts Model for Sequential Recommendation](https://dl.acm.org/doi/abs/10.1145/3701551.3703552) 378 | - [Temporal Linear Item-Item Model for Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3701551.3703554) 379 | - [DimeRec: A Unified Framework for Enhanced Sequential Recommendation via Generative Diffusion Models](https://dl.acm.org/doi/pdf/10.1145/3701551.3703555) 380 | - [Spectrum-based Modality Representation Fusion Graph Convolutional Network for Multi-modal Recommendation](https://dl.acm.org/doi/pdf/10.1145/3701551.3703561) 381 | - [Sequentially Diversified and Accurate Recommendations in Chronological Order for a Series of Users](https://dl.acm.org/doi/pdf/10.1145/3701551.3703564) 382 | - [HaGAR: Hardness-aware Generative Adversarial Recommender](https://dl.acm.org/doi/pdf/10.1145/3701551.3703569) 383 | - [DLCRec: A Novel Approach for Managing Diversity in LLM-Based Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3701551.3703572) 384 | - [Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendation](https://dl.acm.org/doi/pdf/10.1145/3701551.3703573) 385 | - [Adaptive Loss-based Curricula for Neural Team Recommendation](https://dl.acm.org/doi/pdf/10.1145/3701551.3703574) 386 | - [How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective](https://dl.acm.org/doi/pdf/10.1145/3701551.3703579) 387 | - [Your causal self-attentive recommender hosts a lonely neighborhood](https://dl.acm.org/doi/pdf/10.1145/3701551.3703587) 388 | 389 | 390 | ## CIKM 2025 391 | - [STARec: An Efficient Agent Framework for Recommender](https://arxiv.org/pdf/2508.18812) 392 | - [SPARK: Adaptive Low-Rank Knowledge Graph Modeling in Hybrid Geometric Spaces for Recommendation](https://arxiv.org/pdf/2509.11094) 393 | - [Improving Recommendation Fairness via Graph Structure and Representation Augmentation](https://arxiv.org/pdf/2508.19547) 394 | - [MARM: Unlocking the Future of Recommendation Systems through Memory Augmentation and Scalable Complexity](https://arxiv.org/pdf/2411.09425) 395 | - [Is This News Still Interesting to You?: Lifetime-aware Interest Matching for News Recommendation](https://arxiv.org/pdf/2508.13064) 396 | - [What Data is Really Necessary? A Feasibility Study of Inference Data Minimization for Recommender Systems](https://arxiv.org/pdf/2508.21547) 397 | - [Energy-Guided Diffusion Sampling for Long-Term User Behavior Prediction in Reinforcement Learning-based Recommendation]() 398 | - [Hierarchical Multi-View Contrastive Learning for Bundle Recommendation]() 399 | - [PAnDA: Combating Negative Augmentation via Large Language Models for User Cold-Start Recommendations]() 400 | - [Twin-Flow Generative Ranking Network for Recommendation]() 401 | - [Content-Agnostic Moderation for Stance-Neutral Recommendations](https://arxiv.org/pdf/2405.18941) 402 | - [LeadFairRec: LLM-enhanced Discriminative Counterfactual Debiasing for Two-sided Fairness in Recommendation]() 403 | - [EvalAgent: Towards Evaluating News Recommender Systems with LLM-based Agents]() 404 | - [Scenario-Wise Rec: A Multi-Scenario Recommendation Benchmark](https://arxiv.org/pdf/2412.17374) 405 | - [PriviRec: Confidential and Decentralized Graph Filtering for Recommender Systems]() 406 | - [Causality-aware Graph Aggregation Weight Estimator for Popularity Debiasing in Top-K Recommendation]() 407 | - [Usefulness and Diminishing Returns: Evaluating Social Information in Recommender Systems]() 408 | - [Empowering Large Language Model for Sequential Recommendation via Multimodal Embeddings and Semantic IDs](https://arxiv.org/pdf/2509.02017) 409 | - [Hypercomplex Prompt-aware Multimodal Recommendation](https://arxiv.org/pdf/2508.10753) 410 | - [TCFMamba: Trajectory Collaborative Filtering Mamba for Debiased Point-of-Interest Recommendation]() 411 | - [Mitigating Dual Latent Confounding Bias in Recommender Systems](https://arxiv.org/pdf/2410.12451) 412 | - [Compensating Information and Capturing Modal Preferences in Multimodal Recommendation: A Dual-Path Representation Learning Framework]() 413 | - [STEP: Stepwise Curriculum Learning for Context-Knowledge Fusion in Conversational Recommendation](https://arxiv.org/pdf/2508.10669) 414 | - [MUFFIN: Mixture of User-Adaptive Frequency Filtering for Sequential Recommendation](https://arxiv.org/pdf/2508.13670) 415 | - [EEG-FSL: An EEG-Based Few-Shot Learning Framework for Music Recommendation]() 416 | - [Personalized Federated Recommendation with Multi-Faceted User Representation and Global Consistent Prototype]() 417 | - [M-LLM^3REC: A Motivation-Aware User-Item Interaction Framework for Enhancing Recommendation Accuracy with LLMs](https://arxiv.org/pdf/2508.15262) 418 | - [Frequency-Domain Disentanglement-Fusion and Dual Contrastive Learning for Sequential Recommendation]() 419 | - [Modality Alignment with Multi-scale Bilateral Attention for Multimodal Recommendation](https://arxiv.org/pdf/2509.09114) 420 | - [Frequency-Decoupled Distillation for Efficient Multimodal Recommendation]() 421 | - [Generative Data Augmentation in Graph Contrastive Learning for Recommendation]() 422 | - [ExplorAct: Context-Aware Next Action Recommendations for Interactive Data Exploration]() 423 | - [Collaborative Interest Mining Network for Knowledge Graph-based Recommendation]() 424 | - [Learning Invariant Reliability under Diverse Contexts for Robust Multimedia Recommendation]() 425 | - [Evaluating and Addressing Fairness Across User Groups in Negative Sampling for Recommender Systems]() 426 | - [Enhancing Recommendation with Reliable Multi-profile Alignment and Collaborative-aware Contrastive Learning]() 427 | - [Federated Continual Recommendation](https://arxiv.org/pdf/2508.04792) 428 | - [DT-FedSDC: A Dual-Target Federated Framework with Semantic Enhancement and Disentangled Contrastive Learning for Cross-Domain Recommendation]() 429 | - [Context-aware Sequential Bundle Recommendation via User-specific Representations]() 430 | - [Local Large Language Models for Recommendation](http://www.joonseok.net/papers/l3rec.pdf) 431 | - [Higher-order Structure and Semantics-enhanced User Profiling for Recommendation]() 432 | - [A Self-Supervised Mixture-of-Experts Framework for Multi-behavior Recommendation](https://arxiv.org/pdf/2508.19507) 433 | - [PKGRec: Personal Knowledge Graph Construction and Mining for Federated Recommendation Enhancement]() 434 | - [MI4Rec: Pretrained Language Model based Cold-Start Recommendation with Meta-Item Embeddings]() 435 | - [Benefit from Rich: Tackling Search Interaction Sparsity in Search Enhanced Recommendation](https://arxiv.org/pdf/2508.04145) 436 | - [Crocodile: Cross Experts Covariance for Disentangled Learning in Multi-Domain Recommendation](https://arxiv.org/pdf/2405.12706) 437 | - [Maximum In-Support Return Modeling for Dynamic Recommendation with Language Model Prior]() 438 | - [Linking Ordered and Orderless Modeling for Sequential Recommendation]() 439 | - [Enhancing Dual-Target Cross-Domain Recommendation via Similar User Bridging]() 440 | - [LangPTune: Optimizing Language-based User Profiles for Recommendation]() 441 | - [Community-Aware Social Community Recommendation](https://arxiv.org/pdf/2508.05107) 442 | - [SELF: Surrogate-light Feature Selection with Large Language Models in Deep Recommender Systems]() 443 | - [Autonomous Reasoning-Retrieval for Large Language Model Based Recommendation]() 444 | - [Incremental Learning for LLM-based Tokenization and Recommendation]() 445 | - [Efficient Multimodal Streaming Recommendation via Expandable Side Mixture-of-Experts](https://arxiv.org/pdf/2508.05993) 446 | - [Continuous Data Augmentation via Condition-Tokenized Diffusion Transformer for Sequential Recommendation]() 447 | - [Do Recommender Systems Really Leverage Multimodal Content? A Comprehensive Analysis on Multimodal Representations for Recommendation](https://arxiv.org/pdf/2508.04571) 448 | - [Empowering Denoising Sequential Recommendation with Large Language Model Embeddings]() 449 | - [A Hierarchical Structure-Enhanced Personalized Recommendation Model for Traditional Chinese Medicine Formulas Based on KG Diffusion Guidance]() 450 | - [QARM: Quantitative Alignment Multi-Modal Recommendation at Kuaishou](https://arxiv.org/pdf/2411.11739) 451 | - [Stratified Expert Cloning for Retention-Aware Recommendation at Scale](https://arxiv.org/pdf/2504.05628) 452 | - [RankMixer: Scaling Up Ranking Models in Industrial Recommenders](https://arxiv.org/pdf/2507.15551) 453 | - [Meta-Adaptive Network for Effective Cold-Start Recommendation via Warm-Aware Representation Learning]() 454 | - [Waypoint POI Recommendation for Vehicle Navigation Services using Hierarchical Graphs and Contrastive Learning]() 455 | - [Personalized Multi Modal Alignment Encoding for CTR-Recommendation in WeChat]() 456 | - [Augmenting Guest Search Results with Recommendations at Airbnb]() 457 | - [MISS: Multi-Modal Tree Indexing and Searching with Lifelong Sequential Behavior for Retrieval Recommendation](https://arxiv.org/pdf/2508.14515) 458 | - [DAS: Dual-Aligned Semantic IDs Empowered Industrial Recommender System](https://arxiv.org/pdf/2508.10584) 459 | - [TRAWL: External Knowledge-Enhanced Recommendation with LLM Assistance](https://arxiv.org/pdf/2403.06642) 460 | - [Personalized Tree based progressive regression model for watch-time prediction in short video recommendation]() 461 | - [Billion-Scale Graph Deep Learning Framework for Ads Recommendation at Meta]() 462 | - [Next-Generation Price Recommendation with LLM-Augmented Graph Transformers]() 463 | - [TBGRecall: A Generative Retrieval Model for E-commerce Recommendation Scenarios](https://arxiv.org/pdf/2508.11977) 464 | - [Climber: Toward Efficient Scaling Laws for Large Recommendation Models]() 465 | - [Taming Ultra-Long Behavior Sequence in Session-wise Generative Recommendation]() 466 | - [MTGR: Industrial-Scale Generative Recommendation Framework in Meituan](https://arxiv.org/pdf/2505.18654) 467 | - [Towards Unbiased and Real-Time Staytime Prediction for Live Streaming Recommendation]() 468 | - [Progressive Semantic Residual Quantization for Multimodal-Joint Interest Modeling in Music Recommendation](https://arxiv.org/pdf/2508.20359) 469 | - [PRECISE: Pre-training Sequential Recommenders with Collaborative and Semantic Information](https://arxiv.org/pdf/2412.06308) 470 | - [Heterogeneous Influence Maximization in User Recommendation](https://arxiv.org/pdf/2508.13517) 471 | - [Autoregressive Generative Retrieval for Industrial-Scale Recommendations at Pinterest]() 472 | - [Semantic Filter Recommendation for eCommerce Search]() 473 | - [Harnessing Light for Cold-Start Recommendations: Leveraging Epistemic Uncertainty to Enhance Performance in User-Item Interactions]() 474 | - [G2IFS: Global-to-Instance Feature Selection in Deep Recommender System]() 475 | - [A Soft-partitioned Semi-supervised Collaborative Transfer Learning Approach for Multi-Domain Recommendation]() 476 | - [Multi-Item-Query Attention for Stable Sequential Recommendation]() 477 | - [Active Recommendation for Email Outreach Dynamics]() 478 | - [Asymmetric Diffusion Recommendation Model](https://arxiv.org/pdf/2508.12706) 479 | - [Multi-modal Adaptive Mixture of Experts for Cold-start Recommendation](https://arxiv.org/pdf/2508.08042) 480 | - [Bayesian Privacy Guarantee for User History in Sequential Recommendation Using Randomised Response]() 481 | - [Anchor-based Pairwise Comparison via Large Language Model for Recommendation Reranking]() 482 | - [Multi-Behavior Intent Disentanglement for Recommendation via Information Bottleneck Principle]() 483 | - [Social Relation Meets Recommendation: Augmentation and Alignment]() 484 | - [Temporal-Aware User Behaviour Simulation with Large Language Models for Recommender Systems](https://iron13.github.io/paper/C18.pdf) 485 | - [Side Information Memory Network: Expanding the Breadth of User Behavior Sequences in Recommendation]() 486 | - [A Universal Framework for Offline Serendipity Evaluation in Recommender Systems via Large Language Models](https://arxiv.org/pdf/2508.17571) 487 | - [Counterfactual Inference for Eliminating Sentiment Bias in Recommender Systems](https://arxiv.org/pdf/2505.03655) 488 | - [Ultra Fast Warm Start Solution for Graph Recommendations](https://arxiv.org/pdf/2509.01549) 489 | - [Leveraging Large Language Models for Complementary Product Ads Recommendation]() 490 | - [LLM-Enhanced Linear Autoencoders for Recommendation](https://arxiv.org/pdf/2508.13500) 491 | - [Time-Period-Aware Embedding Regeneration for Session-Based Recommendation]() 492 | - [Sparse Autoencoders in Collaborative Filtering Enhanced LLM-based Recommender Systems]() 493 | 494 | 495 | ## WWW 2025 496 | - [G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendation](https://openreview.net/pdf?id=JSSeMdhsye) 497 | - [On-device Content-based Recommendation with Single-shot Embedding Pruning: A Cooperative Game Perspective](https://openreview.net/pdf?id=k03hiubX3F) 498 | - [Frequency-Augmented Mixture-of-Heterogeneous-Experts Framework for Sequential Recommendation](https://openreview.net/pdf?id=6Mgc1ZLDZt) 499 | - [Rankformer: A Graph Transformer for Recommendation based on Ranking Objective](https://openreview.net/pdf?id=rdnzTocXBr) 500 | - [Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential Recommendation](https://openreview.net/pdf?id=ty7Qk12Pd8) 501 | - [Hyperbolic Variational Graph Auto-Encoder for Next POI Recommendation](https://openreview.net/pdf?id=uEladLyKgE) 502 | - [Distinguished Quantized Guidance for Diffusion-based Sequence Recommendation](https://openreview.net/pdf?id=L8MeU0K5Fx) 503 | - [Dual Graph Denoising Model for Social Recommendation](https://openreview.net/pdf?id=CKJXHFvm3v) 504 | - [GraphHash: Graph Clustering Enables Parameter Efficiency in Recommender Systems](https://openreview.net/pdf?id=U3TzIAg5Dg) 505 | - [Hypergraph-based Temporal Modelling of Repeated Intent for Sequential Recommendation](https://openreview.net/pdf?id=t0q7KbmB7o) 506 | - [ITMPRec: Intention-based Targeted Multi-round Proactive Recommendation](https://openreview.net/pdf?id=MipDf3C38E) 507 | - [Large Language Models as Narrative-Driven Recommenders](https://openreview.net/pdf?id=Bgr4RHYONd) 508 | - [LLM-BS: Enhancing Large Language Models for Recommendation through Exogenous Behavior-Semantics Integration](https://openreview.net/pdf?id=rm07DoACiF) 509 | - [Reembedding and Reweighting are Needed for Tail Item Sequential Recommendation](https://openreview.net/pdf?id=zc1XEMHbeO) 510 | - [Generating with Fairness: A Modality-Diffused Counterfactual Framework for Incomplete Multimodal Recommendations](https://openreview.net/pdf?id=PsVEUofCZE) 511 | - [Explainable Multi-Modality Alignment for Transferable Recommendation](https://openreview.net/pdf?id=5qxBSIA0l3) 512 | - [Heterogeneous Graph Transfer Learning for Category-aware Cross-Domain Sequential Recommendation](https://openreview.net/pdf?id=RTjTPTbH3g) 513 | - [MixRec: Individual and Collective Mixing Empowers Data Augmentation for Recommender Systems](https://openreview.net/pdf?id=rNvMJEgcu2) 514 | - [When Large Vision Language Models Meet Multimodal Sequential Recommendation: An Empirical Study](https://openreview.net/pdf?id=E8bjWloEvU) 515 | - [A Plug-in Critiquing Approach for Knowledge Graph Recommendation Systems via Representative Sampling.](https://openreview.net/pdf?id=bgfXzR8bBF) 516 | - [CROWN: A Novel Approach to Comprehending Users' Preferences for Accurate Personalized News Recommendation](https://openreview.net/pdf?id=hvgN6AeeXt) 517 | - [Does weighting improve matrix factorization for recommender systems?](https://openreview.net/pdf?id=mxIGQ0bIum) 518 | - [Hyperbolic Diffusion Recommender Model](https://openreview.net/pdf?id=oQU1OrqNl7) 519 | - [Personalized Denoising Implicit Feedback for Robust Recommender System](https://openreview.net/pdf?id=U3jslTfxTm) 520 | - [Privacy-Friendly Cross-Domain Recommendation via Distilling User-irrelevant Information](https://openreview.net/pdf?id=yuzbzzekdB) 521 | - [Unleash LLMs Potential for Sequential Recommendation by Coordinating Dual Dynamic Index Mechanism](https://openreview.net/pdf?id=GE71TxvTH3) 522 | - [A LLM-based Controllable, Scalable, Human-Involved User Simulator Framework for Conversational Recommender Systems](https://openreview.net/pdf?id=H9BYGURN9M) 523 | - [ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain Sequential Recommendation](https://openreview.net/pdf?id=5AHO1syvXl) 524 | - [Aegis: Post-Training Attribute Unlearning in Federated Recommender Systems against Attribute Inference Attacks](https://openreview.net/pdf?id=OHtjMJABg0) 525 | - [Beyond Utility: Evaluating LLM as Recommender](https://openreview.net/pdf?id=YiIdHqqoCd) 526 | - [Bridging the Gap: Teacher-Assisted Wasserstein Knowledge Distillation for Efficient Multi-Modal Recommendation](https://openreview.net/pdf?id=ss9UXxbSys) 527 | - [Collaborative Retrieval for Large Language Model-based Conversational Recommender Systems](https://openreview.net/pdf?id=cHTWkVH5ft) 528 | - [Distributionally Robust Graph Out-of-Distribution Recommendation via Diffusion Model](https://openreview.net/pdf?id=b2skS0nzFO) 529 | - [Filtering Discomforting Recommendations with Large Language Models](https://openreview.net/pdf?id=T2iIppZjhp) 530 | - [Graph Representation Learning via Causal Diffusion for Out-of-Distribution Recommendation](https://openreview.net/pdf?id=8O8eqpaCAI) 531 | - [LLM4Rerank: LLM-based Auto-Reranking Framework for Recommendations](https://openreview.net/pdf?id=HEBVEmK22u) 532 | - [Model-Agnostic Social Network Refinement with Diffusion Models for Robust Social Recommendation](https://openreview.net/pdf?id=Kj2dqxyH8O) 533 | - [Personalized Federated Recommendation for Cold-Start Users via Adaptive Knowledge Fusion](https://openreview.net/pdf?id=bhWngwuo74) 534 | - [Plug and Play: Enabling Pluggable Attribute Unlearning in Recommender Systems](https://openreview.net/pdf?id=8LuVZOMqF6) 535 | - [TEARS: Text Representations for Scrutable Recommendations](https://openreview.net/pdf?id=cMn2KCzjaX) 536 | - [Uncertainty Quantification and Decomposition for LLM-based Recommendation](https://openreview.net/pdf?id=lhFcbb5q48) 537 | - [Unleashing the Power of Large Language Model for Denoising Recommendation](https://openreview.net/pdf?id=doyAPsYKf6) 538 | - [Value Function Decomposition in Markov Recommendation Process](https://openreview.net/pdf?id=eiKc2SdjSS) 539 | - [Optimizing Revenue through User Coupon Recommendations in Truthful Online Ad Auctions](https://openreview.net/pdf?id=W4dHcodACr) 540 | - [Towards Efficient Conversational Recommendations: Expected Value of Information Meets Bandit Learning](https://openreview.net/pdf?id=xT9Jy2d6Sd) 541 | - [Unleashing the Potential of Multi-Channel Fusion in Retrieval for Personalized Recommendations](https://openreview.net/pdf?id=URmGwzxCC1) 542 | - [Criteria-Aware Graph Filtering: Extremely Fast Yet Accurate Multi-Criteria Recommendation](https://openreview.net/pdf?id=Sem9fdWZlq) 543 | - [D2K: Turning Historical Data into Retrievable Knowledge for Recommender Systems](https://openreview.net/pdf?id=4AumMJKets) 544 | - [Joint Evaluation of Fairness and Relevance in Recommender Systems with Pareto Frontier](https://openreview.net/pdf?id=1Hfhp9BhI5) 545 | - [Joint Similarity Item Exploration and Overlapped User Guidance for Multi-Modal Cross-Domain Recommendation](https://openreview.net/pdf?id=m7SmS3Rkr5) 546 | - [Policy-Guided Causal State Representation for Offline Reinforcement Learning Recommendation](https://openreview.net/pdf?id=8QJCZmycIS) 547 | - [TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential Recommendation](https://openreview.net/pdf?id=pKOJrwqXLh) 548 | - [Disentangling Likes and Dislikes in Personalized Generative Explainable Recommendation](https://openreview.net/pdf?id=UhPUR9cnRJ) 549 | - [DVIB: Towards Robust Multimodal Recommender Systems via Variational Information Bottleneck Distillation](https://openreview.net/pdf?id=k4e3Dh2icw) 550 | - [P4GCN: Vertical Federated Social Recommendation with Privacy-Preserving Two-Party Graph Convolution Network](https://openreview.net/pdf?id=j7B1HkhSox) 551 | - [xMTF: A Formula-Free Model for Reinforcement-Learning-Based Multi-Task Fusion in Recommender Systems](https://openreview.net/pdf?id=WTYgCjmCKQ) 552 | - [A Context-Aware Framework for Integrating Ad Auctions and Recommendations](https://openreview.net/pdf?id=XNvag15LFb) 553 | - [Reducing Symbiosis Bias Through Better A/B Tests of Recommendation Algorithms](https://openreview.net/pdf?id=kFO0vRKweC) 554 | - [Interactive Visualization Recommendation with Hier-SUCB](https://openreview.net/pdf?id=Hkh2umURYm) 555 | - [AURO: Reinforcement Learning for Adaptive User Retention Optimization in Recommender Systems](https://openreview.net/pdf?id=5I66GoEqnS) 556 | 557 | 558 | ## IJCAI 2025 559 | - [Guaranteed Top-Adaptive-K in Recommendation](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/1596.pdf) 560 | - [Variational Graph Auto-Encoder Driven Graph Enhancement for Sequential Recommendation](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/66.pdf) 561 | - [Enhancing Long-Tail Bundle Recommendations Utilizing Composition Pattern Modeling](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/302.pdf) 562 | - [Flow-based Time-aware Causal Structure Learning for Sequential Recommendation](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/325.pdf) 563 | - [Beyond Individual and Point: Next POI Recommendation via Region-aware Dynamic Hypergraph with Dual-level Modeling](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/740.pdf) 564 | - [Device-Cloud Collaborative Correction for On-Device Recommendation](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/1430.pdf) 565 | - [Decision-Aware Preference Modeling for Multi-Behavior Recommendation](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/1636.pdf) 566 | - [Disentangled and Personalized Representation Learning for Next Point-of-Interest Recommendation](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/2893.pdf) 567 | - [Cost-Effective On-Device Sequential Recommendation with Spiking Neural Networks](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/3150.pdf) 568 | - [Single-Node Trigger Backdoor Attacks in Graph-Based Recommendation Systems](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/3185.pdf) 569 | - [Flow Matching Based Sequential Recommender Model](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/3263.pdf) 570 | - [Efficient Inter-Operator Scheduling for Concurrent Recommendation Model Inference on GPU](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/3647.pdf) 571 | - [Where and When: Predict Next POI and Its Explicit Timestamp in Sequential Recommendation](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/3700.pdf) 572 | - [Balancing User-Item Structure and Interaction with Large Language Models and Optimal Transport for Multimedia Recommendation](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/4194.pdf) 573 | - [Interaction-Data-guided Conditional Instrumental Variables for Debiasing Recommender Systems](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/4915.pdf) 574 | - [HPDM: A Hierarchical Popularity-aware Debiased Modeling Approach for Personalized News Recommender](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/5401.pdf) 575 | - [Test-Time Adaptation on Recommender System with Data-Centric Graph Transformation](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/5463.pdf) 576 | - [Preference Identification by Interaction Overlap for Bundle Recommendation](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/5482.pdf) 577 | - [Towards Automatic Sampling of User Behaviors for Sequential Recommender Systems](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/6053.pdf) 578 | - [GPL4SRec: Graph Multi-Level Aware Prompt Learning for Streaming Recommendation](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/6286.pdf) 579 | - [CoLA-Former: Graph Transformer Using Communal Linear Attention for Lightweight Sequential Recommendation](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/6354.pdf) 580 | - [Attribute Association Driven Multi-Task Learning for Session-based Recommendation](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/7459.pdf) 581 | - [CLLMRec: Contrastive Learning with LLMs-based View Augmentation for Sequential Recommendation](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/7524.pdf) 582 | - [ARMR: Adaptively Responsive Network for Medication Recommendation](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/7647.pdf) 583 | - [Do Mentioned Items Truly Matter? Enhancing Conversational Recommender Systems with Causal Intervention and Large Language Models](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/7823.pdf) 584 | 585 | ## ICML 2025 586 | 587 | - [ENSUR: Equitable and Statistically Unbiased Recommendation](https://openreview.net/pdf?id=iLm1cNnjzk) 588 | - [Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning](https://openreview.net/pdf?id=0E5rZOGA13) 589 | - [Optimizing Social Network Interventions via Hypergradient-Based Recommender System Design](https://openreview.net/pdf?id=Q4tpr9bnXU) 590 | - [Recommendations with Sparse Comparison Data: Provably Fast Convergence for Nonconvex Matrix Factorization](https://openreview.net/pdf?id=vcNJgiEGdz) 591 | - [Flow Matching for Denoised Social Recommendation](https://openreview.net/pdf?id=gqKz6uCbI1) 592 | - [ActionPiece: Contextually Tokenizing Action Sequences for Generative Recommendation](https://openreview.net/pdf?id=h2oNQOzbc5) 593 | - [Beyond Self-Interest: How Group Strategies Reshape Content Creation in Recommendation Platforms?](https://openreview.net/pdf?id=q0JaH6Ukqb) 594 | - [SAFER: A Calibrated Risk-Aware Multimodal Recommendation Model for Dynamic Treatment Regimes](https://openreview.net/pdf?id=7UqNM85dD6) 595 | - [SecEmb: Sparsity-Aware Secure Federated Learning of On-Device Recommender System with Large Embedding](https://openreview.net/pdf?id=j7H4mbeOI1) 596 | - [MTSTRec: Multimodal Time-Aligned Shared Token Recommender](https://openreview.net/pdf?id=yWDvVl9Wtp) 597 | - [A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings](https://openreview.net/pdf?id=27tMzmzDjO) 598 | - [FACTER: Fairness-Aware Conformal Thresholding and Prompt Engineering for Enabling Fair LLM-Based Recommender Systems](https://openreview.net/pdf?id=edN2rEemj6) 599 | - [Improving LLMs for Recommendation with Out-Of-Vocabulary Tokens](https://openreview.net/pdf?id=cerqDkPLx7) 600 | - [SHARP-Distill: A 68× Faster Recommender System with Hypergraph Neural Networks and Language Models](https://openreview.net/pdf?id=3hYrORJndz) 601 | 602 | 603 | ## AAAI 2025 604 | - `Coming Soon` 605 | 606 | 607 | ## NeurIPS 2025 608 | - `Coming Soon` 609 | 610 | 611 | 612 | # 2023 613 | 614 | ## WSDM 2023 615 | 616 | - [Search Behavior Prediction: A Hypergraph Perspective](https://arxiv.org/pdf/2211.13328.pdf) 617 | - [CL4CTR: A Contrastive Learning Framework for CTR Prediction](https://arxiv.org/pdf/2212.00522.pdf) 618 | - [IDNP: Interest Dynamics Modeling using Generative Neural Processes for Sequential Recommendation](https://arxiv.org/pdf/2208.04600.pdf) 619 | - [Learning to Distinguish Multi-User Coupling Behaviors for TV Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539597.3570374) 620 | - [Towards Universal Cross-Domain Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539597.3570366) 621 | - [One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation](https://arxiv.org/pdf/2211.11964.pdf) 622 | - [Slate-Aware Ranking for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539597.3570380) 623 | - [Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation](https://arxiv.org/pdf/2211.11159.pdf) 624 | - [Knowledge Enhancement for Contrastive Multi-Behavior Recommendation](https://arxiv.org/pdf/2301.05403.pdf) 625 | - [Disentangled Representation for Diversified Recommendations](https://arxiv.org/pdf/2301.05492.pdf) 626 | - [Heterogeneous Graph-based Context-aware Document Ranking](http://playbigdata.ruc.edu.cn/dou/publication/2023_wsdm_session.pdf) 627 | - [Cognition-aware Knowledge Graph Reasoning for Explainable Recommendation](http://playbigdata.ruc.edu.cn/dou/publication/2023_wsdm_congnition.pdf) 628 | - [Separating Examination and Trust Bias from Click Predictions for Unbiased Relevance Ranking](https://dl.acm.org/doi/pdf/10.1145/3539597.3570393) 629 | - [Self-Supervised Group Graph Collaborative Filtering for Group Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539597.3570400) 630 | - [Learning Topical Stance Embeddings from Signed Social Graphs](https://arxiv.org/pdf/2201.11675.pdf) 631 | - [Calibrated Recommendations as a Minimum-Cost Flow Problem](https://dl.acm.org/doi/pdf/10.1145/3539597.3570402) 632 | - [Search Behavior Prediction: A Hypergraph Perspective](https://arxiv.org/pdf/2211.13328.pdf) 633 | - [DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation](https://arxiv.org/pdf/2210.16591.pdf) 634 | - [Multi-Intentions Oriented Contrastive Learning for Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539597.3570411) 635 | - [MUSENET: Multi-Scenario Learning for Repeat-Aware Personalized Recommendation](https://cs.nju.edu.cn/yuanyao/static/wsdm2023.pdf) 636 | - [A Personalized Neighborhood-based Model for Within-basket Recommendation in Grocery Shopping](https://dl.acm.org/doi/pdf/10.1145/3539597.3570417) 637 | - [Disentangled Negative Sampling for Collaborative Filtering](https://dl.acm.org/doi/pdf/10.1145/3539597.3570419) 638 | - [DIGMN: Dynamic Intent Guided Meta Network for Differentiated User Engagement Forecasting in Online Professional Social Platforms](https://arxiv.org/pdf/2210.12402.pdf) 639 | - [SGCCL: Siamese Graph Contrastive Consensus Learning for Personalized Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539597.3570422) 640 | - [Multimodal Pre-Training with Self-Distillation for Product Understanding in E-Commerce](https://dl.acm.org/doi/pdf/10.1145/3539597.3570423) 641 | - [Relation Preference oriented High-order Sampling for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539597.3570424) 642 | - [Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendation](https://arxiv.org/pdf/2212.11868.pdf) 643 | - [Exploiting Explicit and Implicit Item relationships for Session-based Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539597.3570432) 644 | - [Meta Policy Learning for Cold-Start Conversational Recommendation](https://arxiv.org/pdf/2205.11788.pdf) 645 | - [Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network](https://arxiv.org/pdf/2206.12781.pdf) 646 | - [Simplifying Graph-based Collaborative Filtering for Recommendation](https://opus.lib.uts.edu.au/bitstream/10453/164889/3/Simplifying_Graph_based_Collaborative_Filtering_for_Recommendation__WSDM23_.pdf) 647 | - [AutoGen: An Automated Dynamic Model Generation Framework for Recommender System](https://dl.acm.org/doi/pdf/10.1145/3539597.3570456) 648 | - [A Causal View for Item-level Effect of Recommendation on User Preference](https://dl.acm.org/doi/pdf/10.1145/3539597.3570461) 649 | - [Federated Unlearning for On-Device Recommendation](https://arxiv.org/pdf/2210.10958.pdf) 650 | - [Counterfactual Collaborative Reasoning](https://dl.acm.org/doi/pdf/10.1145/3539597.3570464) 651 | - [Uncertainty Quantification for Fairness in Two-Stage Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3539597.3570469) 652 | - [Generating Explainable Product Comparisons for Online Shopping](https://assets.amazon.science/5d/03/2f7e2ab8407cb37e679211c2c677/generating-explainable-product-comparisons-for-online-shopping.pdf) 653 | - [Unbiased Knowledge Distillation for Recommendation](https://arxiv.org/pdf/2211.14729.pdf) 654 | - [VRKG4Rec: Virtual Relational Knowledge Graph for Recommendation](https://arxiv.org/pdf/2204.01089.pdf) 655 | - [Knowledge-Adaptive Contrastive Learning for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539597.3570483) 656 | - [Heterogeneous Graph Contrastive Learning for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539597.3570484) 657 | 658 | ## ICLR 2023 659 | 660 | - [Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems](https://arxiv.org/pdf/2205.09809.pdf) 661 | - [ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual Actor](https://arxiv.org/pdf/2206.02620.pdf) 662 | - [Personalized Reward Learning with Interaction-Grounded Learning (IGL)](https://arxiv.org/pdf/2211.15823.pdf) 663 | - [TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations](https://openreview.net/pdf?id=EIgLnNx_lC) 664 | - [Online Low Rank Matrix Completion](https://arxiv.org/pdf/2209.03997.pdf) 665 | - [StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random](https://arxiv.org/pdf/2205.04701.pdf) 666 | - [MaskFusion: Feature Augmentation for Click-Through Rate Prediction via Input-adaptive Mask Fusion](https://openreview.net/pdf?id=QzbKH8nNq_V) 667 | - [LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation](https://openreview.net/pdf?id=FKXVK9dyMM) 668 | 669 | ## AAAI 2023 670 | 671 | - [Scaling Law for Recommendation Models: Towards General-purpose User Representations](https://arxiv.org/pdf/2111.11294.pdf) 672 | 673 | ## WWW 2023 674 | 675 | - [Multi-Modal Self-Supervised Learning for Recommendation](https://arxiv.org/pdf/2302.10632.pdf) 676 | - [Collaboration-Aware Graph Convolutional Network for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3543507.3583229) 677 | - [Enhancing Hierarchy-Aware Graph Networks with Deep Dual Clustering for Session-based Recommendation](https://dl.acm.org/doi/pdf/10.1145/3543507.3583247) 678 | - [ConsRec: Learning Consensus Behind Interactions for Group Recommendation](https://dl.acm.org/doi/pdf/10.1145/3543507.3583277) 679 | - [Semi-decentralized Federated Ego Graph Learning for Recommendation](https://arxiv.org/pdf/2302.10900.pdf) 680 | - [Joint Internal Multi-Interest Exploration and External Domain Alignment for Cross Domain Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3543507.3583366) 681 | - [Intra and Inter Domain HyperGraph Convolutional Network for Cross-Domain Recommendation](https://dl.acm.org/doi/pdf/10.1145/3543507.3583402) 682 | - [Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation](https://dl.acm.org/doi/pdf/10.1145/3543507.3583526) 683 | - [ApeGNN: Node-Wise Adaptive Aggregation in GNNs for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3543507.3583530) 684 | - [Enhancing User Personalization in Conversational Recommenders](https://arxiv.org/pdf/2302.06656.pdf) 685 | - [LINet: A Location and Intention-Aware Neural Network for Hotel Group Recommendation](https://dl.acm.org/doi/pdf/10.1145/3543507.3583202) 686 | - [Multi-Modal Self-Supervised Learning for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3543507.3583206) 687 | - [Distillation from Heterogeneous Models for Top-K Recommendation](https://dl.acm.org/doi/pdf/10.1145/3543507.3583209) 688 | - [On the Theories Behind Hard Negative Sampling for Recommendation](https://arxiv.org/pdf/2302.03472.pdf) 689 | - [Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation](https://dl.acm.org/doi/pdf/10.1145/3543507.3583240) 690 | - [Exploration and Regularization of the Latent Action Space in Recommendation](https://arxiv.org/pdf/2302.03431.pdf) 691 | - [Bootstrap Latent Representations for Multi-modal Recommendation](https://dl.acm.org/doi/pdf/10.1145/3543507.3583251) 692 | - [Two-Stage Constrained Actor-Critic for Short Video Recommendation](https://dl.acm.org/doi/pdf/10.1145/3543507.3583259) 693 | - [Recommendation with Causality enhanced Natural Language Explanations](https://dl.acm.org/doi/pdf/10.1145/3543507.3583260) 694 | - [Cross-domain recommendation via user interest alignment](https://arxiv.org/pdf/2301.11467.pdf) 695 | - [Robust Recommendation with Adversarial Gaussian Data Augmentation](https://dl.acm.org/doi/pdf/10.1145/3543507.3583273) 696 | - [Dual-interest Factorization-heads Attention for Sequential Recommendation](https://arxiv.org/pdf/2302.03965.pdf) 697 | - [Contrastive Collaborative Filtering for Cold-Start Item Recommendation](https://arxiv.org/pdf/2302.02151.pdf) 698 | - [Anti-FakeU: Defending Shilling Attacks on Graph Neural Network based Recommender Model](https://dl.acm.org/doi/pdf/10.1145/3543507.3583289) 699 | - [Compressed Interaction Graph based Framework for Multi-behavior Recommendation](https://arxiv.org/pdf/2303.02418.pdf) 700 | - [A Counterfactual Collaborative Session-based Recommender System](https://arxiv.org/pdf/2301.13364.pdf) 701 | - [Correlative Preference Transfer with Hierarchical Hypergraph Network for Multi-Domain Recommendation](https://arxiv.org/pdf/2211.11191.pdf) 702 | - [Automated Self-Supervised Learning for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3543507.3583336) 703 | - [AutoDenoise: Automatic Data Instance Denoising for Recommendations](https://arxiv.org/pdf/2303.06611.pdf) 704 | - [Improving Recommendation Fairness via Data Augmentation](https://arxiv.org/pdf/2302.06333.pdf) 705 | - [ColdNAS: Search to Modulate for User Cold-Start Recommendation: ColdNAS](https://dl.acm.org/doi/pdf/10.1145/3543507.3583344) 706 | - [AutoS2AE: Automate to Regularize Sparse Shallow Autoencoders for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3543507.3583349) 707 | - [Quantize Sequential Recommenders Without Private Data](https://dl.acm.org/doi/pdf/10.1145/3543507.3583351) 708 | - [Interaction-level Membership Inference Attack Against Federated Recommender Systems](https://arxiv.org/pdf/2301.10964.pdf) 709 | - [Debiased Contrastive Learning for Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3543507.3583361) 710 | - [Clustered Embedding Learning for Recommender Systems](https://arxiv.org/pdf/2302.01478.pdf) 711 | - [Adap-τ: Adaptively Modulating Embedding Magnitude for Recommendation](https://arxiv.org/pdf/2302.04775.pdf) 712 | - [Robust Preference-Guided Denoising for Graph based Social Recommendation](https://arxiv.org/pdf/2303.08346.pdf) 713 | - [MMMLP: Multi-modal Multilayer Perceptron for Sequential Recommendations](https://dl.acm.org/doi/pdf/10.1145/3543507.3583378) 714 | - [Few-shot News Recommendation via Cross-lingual Transfer](https://dl.acm.org/doi/pdf/10.1145/3543507.3583383) 715 | - [User Retention-oriented Recommendation with Decision Transformer](https://arxiv.org/pdf/2303.06347.pdf) 716 | - [Cooperative Retriever and Ranker in Deep Recommenders](https://dl.acm.org/doi/pdf/10.1145/3543507.3583422) 717 | - [Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders](https://dl.acm.org/doi/pdf/10.1145/3543507.3583434) 718 | - [Show Me The Best Outfit for A Certain Scene: A Scene-aware Fashion Recommender System](https://dl.acm.org/doi/pdf/10.1145/3543507.3583435) 719 | - [Multi-Behavior Recommendation with Cascading Graph Convolutional Network](https://dl.acm.org/doi/pdf/10.1145/3543507.3583439) 720 | - [AutoMLP: Automated MLP for Sequential Recommendations](https://arxiv.org/pdf/2303.06337.pdf) 721 | - [NASRec: Weight Sharing Neural Architecture Search for Recommender Systems](https://arxiv.org/pdf/2207.07187.pdf) 722 | - [Membership Inference Attacks Against Sequential Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3543507.3583447) 723 | - [Communicative MARL-based Relevance Discerning Network for Repetition-Aware Recommendation](https://dl.acm.org/doi/pdf/10.1145/3543507.3583459) 724 | - [Modeling Temporal Positive and Negative Excitation for Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3543507.3583463) 725 | - [Multi-Task Recommendations with Reinforcement Learning](https://dl.acm.org/doi/pdf/10.1145/3543507.3583467) 726 | - [A Self-Correcting Sequential Recommender](https://arxiv.org/pdf/2303.02297.pdf) 727 | - [Cross-domain Recommendation with Behavioral Importance Perception](http://mn.cs.tsinghua.edu.cn/xinwang/PDF/papers/2023_Cross-domain%20Recommendation%20with%20Behavioral%20Importance%20Perception.pdf) 728 | - [Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations](https://arxiv.org/pdf/2304.09085.pdf) 729 | - [Code Recommendation for Open Source Software Developers](https://arxiv.org/pdf/2210.08332.pdf) 730 | - [Denoising and Prompt-Tuning for Multi-Behavior Recommendation](https://arxiv.org/pdf/2302.05862.pdf) 731 | - [Mutual Wasserstein Discrepancy Minimization for Sequential Recommendation](https://arxiv.org/pdf/2301.12197.pdf) 732 | - [Confident Action Decision via Hierarchical Policy Learning for Conversational Recommendation](https://dl.acm.org/doi/pdf/10.1145/3543507.3583536) 733 | - [CAMUS: Attribute-Aware Counterfactual Augmentation for Minority Users in Recommendation](https://dl.acm.org/doi/pdf/10.1145/3543507.3583538) 734 | - [Dynamically Expandable Graph Convolution for Streaming Recommendation](https://arxiv.org/pdf/2303.11700.pdf) 735 | - [Dual Policy Learning for Aggregation Optimization in Graph Neural Network-based Recommender Systems](https://arxiv.org/pdf/2302.10567.pdf) 736 | - [Automatic Feature Selection By One-Shot Neural Architecture Search In Recommendation Systems](https://dl.acm.org/doi/pdf/10.1145/3543507.3583444) 737 | - [Semi-supervised Adversarial Learning for Complementary Item Recommendation](https://arxiv.org/pdf/2303.05812.pdf) 738 | - [RL-MPCA: A Reinforcement Learning Based Multi-Phase Computation Allocation Approach for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3543507.3583313) 739 | - [Learning with Exposure Constraints in Recommendation Systems](https://arxiv.org/pdf/2302.01377.pdf) 740 | - [Maximizing Submodular Functions for Recommendation in the Presence of Biases](https://dl.acm.org/doi/pdf/10.1145/3543507.3583195) 741 | - [Scoping Fairness Objectives and Identifying Fairness Metrics for Recommender Systems: The Practitioners’ Perspective](https://dl.acm.org/doi/pdf/10.1145/3543507.3583204) 742 | - [P-MMF: Provider Max-min Fairness Re-ranking in Recommender System](https://arxiv.org/pdf/2303.06660.pdf) 743 | - [Fairly Adaptive Negative Sampling for Recommendations](https://arxiv.org/pdf/2302.08266.pdf) 744 | 745 | ## Recsys 2023 746 | 747 | - [A Lightweight Method for Modeling Confidence in Recommendations with Learned Beta Distributions](https://dl.acm.org/doi/pdf/10.1145/3604915.3608788) 748 | - [HUMMUS: A Linked, Healthiness-Aware, User-centered and Argument-Enabling Recipe Data Set for Recommendation](https://hal.science/hal-04220182v1/file/3604915.3609491.pdf) 749 | - [Fast and Examination-agnostic Reciprocal Recommendation in Matching Markets](https://dl.acm.org/doi/pdf/10.1145/3604915.3608774) 750 | - [Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations](https://arxiv.org/pdf/2307.06576.pdf) 751 | - [Masked and Swapped Sequence Modeling for Next Novel Basket Recommendation in Grocery Shopping](https://dl.acm.org/doi/pdf/10.1145/3604915.3608803) 752 | - [Full Index Deep Retrieval: End-to-End User and Item Structures for Cold-start and Long-tail Item Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608773) 753 | - [SPARE: Shortest Path Global Item Relations for Efficient Session-based Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608768) 754 | - [Accelerating Creator Audience Building through Centralized Exploration](https://dl.acm.org/doi/pdf/10.1145/3604915.3608880) 755 | - [Beyond Labels: Leveraging Deep Learning and LLMs for Content Metadata](https://dl.acm.org/doi/pdf/10.1145/3604915.3608883) 756 | - [Distribution-based Learnable Filters with Side Information for Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608782) 757 | - [Reciprocal Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608798) 758 | - [STRec: Sparse Transformer for Sequential Recommendations](https://dl.acm.org/doi/pdf/10.1145/3604915.3608779) 759 | - [Track Mix Generation on Music Streaming Services using Transformers](https://dl.acm.org/doi/pdf/10.1145/3604915.3608869) 760 | - [gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative Sampling](https://dl.acm.org/doi/pdf/10.1145/3604915.3608783) 761 | - [Equivariant Contrastive Learning for Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608786) 762 | - [Contrastive Learning with Frequency-Domain Interest Trends for Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608790) 763 | - [Task Aware Feature Extraction Framework for Sequential Dependence Multi-Task Learning](https://dl.acm.org/doi/pdf/10.1145/3604915.3608772) 764 | - [Gradient Matching for Categorical Data Distillation in CTR Prediction](https://dl.acm.org/doi/pdf/10.1145/3604915.3608769) 765 | - [Deep Situation-Aware Interaction Network for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3604915.3608793) 766 | - [AutoOpt: Automatic Hyperparameter Scheduling and Optimization for Deep Click-through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3604915.3608800) 767 | - [Loss Harmonizing for Multi-Scenario CTR Prediction](https://dl.acm.org/doi/pdf/10.1145/3604915.3608865) 768 | - [When Fairness meets Bias: a Debiased Framework for Fairness aware Top-N Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608770) 769 | - [Towards Robust Fairness-aware Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608784) 770 | - [Two-sided Calibration for Quality-aware Responsible Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608799) 771 | - [RecAD: Towards A Unified Library for Recommender Attack and Defense](https://dl.acm.org/doi/pdf/10.1145/3604915.3609490) 772 | - [Adversarial Collaborative Filtering for Free](https://dl.acm.org/doi/pdf/10.1145/3604915.3608771) 773 | - [Augmented Negative Sampling for Collaborative Filtering](https://dl.acm.org/doi/pdf/10.1145/3604915.3608811) 774 | - [Efficient Data Representation Learning in Google-scale Systems](https://dl.acm.org/doi/pdf/10.1145/3604915.3608882) 775 | - [The Effect of Third Party Implementations on Reproducibility](https://dl.acm.org/doi/pdf/10.1145/3604915.3609487) 776 | - [Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3604915.3608766) 777 | - [Trending Now: Modeling Trend Recommendations](https://dl.acm.org/doi/pdf/10.1145/3604915.3608810) 778 | - [Investigating the effects of incremental training on neural ranking models](https://dl.acm.org/doi/pdf/10.1145/3604915.3608872) 779 | - [Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608806) 780 | - [LightSAGE: Graph Neural Networks for Large Scale Item Retrieval in Shopee’s Advertisement Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608863) 781 | - [Multi-Relational Contrastive Learning for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608807) 782 | - [Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis](https://dl.acm.org/doi/pdf/10.1145/3604915.3609489) 783 | - [Goal-Oriented Multi-Modal Interactive Recommendation with Verbal and Non-Verbal Relevance Feedback](https://dl.acm.org/doi/pdf/10.1145/3604915.3608775) 784 | - [Alleviating the Long-Tail Problem in Conversational Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3604915.3608812) 785 | - [Data-free Knowledge Distillation for Reusing Recommendation Models](https://dl.acm.org/doi/pdf/10.1145/3604915.3608789) 786 | - [Reward innovation for long-term member satisfaction](https://dl.acm.org/doi/pdf/10.1145/3604915.3608873) 787 | - [Contextual Multi-Armed Bandit for Email Layout Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608878) 788 | - [Online Matching: A Real-time Bandit System for Large-scale Recommendations](https://dl.acm.org/doi/pdf/10.1145/3604915.3608792) 789 | - [Incentivizing Exploration in Linear Contextual Bandits under Information Gap](https://dl.acm.org/doi/pdf/10.1145/3604915.3608794) 790 | - [AdaptEx: A Self-Service Contextual Bandit Platform](https://dl.acm.org/doi/pdf/10.1145/3604915.3608870) 791 | - [InTune: Reinforcement Learning-based Data Pipeline Optimization for Deep Recommendation Models](https://dl.acm.org/doi/pdf/10.1145/3604915.3608778) 792 | - [Generative Learning Plan Recommendation for Employees: A Performance-aware Reinforcement Learning Approach](https://dl.acm.org/doi/pdf/10.1145/3604915.3608795) 793 | - [Correcting for Interference in Experiments: A Case Study at Douyin](https://dl.acm.org/doi/pdf/10.1145/3604915.3608808) 794 | - [Reproducibility of Multi-Objective Reinforcement Learning Recommendation: Interplay between Effectiveness and Beyond-Accuracy Perspectives](https://dl.acm.org/doi/pdf/10.1145/3604915.3609493) 795 | - [DREAM: Decoupled Representation via Extraction Attention Module and Supervised Contrastive Learning for Cross-Domain Sequential Recommender](https://dl.acm.org/doi/pdf/10.1145/3604915.3608780) 796 | - [A Multi-view Graph Contrastive Learning Framework for Cross-Domain Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608785) 797 | - [Exploring False Hard Negative Sample in Cross-Domain Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608791) 798 | - [Domain Disentanglement with Interpolative Data Augmentation for Dual-Target Cross-Domain Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608802) 799 | - [Uncovering User Interest from Biased and Noised Watch Time in Video Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608797) 800 | - [Understanding and Modeling Passive-Negative Feedback for Short-video Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608814) 801 | - [Personalised Recommendations for the BBC iPlayer: Initial approach and current challenges](https://dl.acm.org/doi/pdf/10.1145/3604915.3608867) 802 | - [Reproducibility Analysis of Recommender Systems relying on Visual Features: traps, pitfalls, and countermeasures](https://dl.acm.org/doi/pdf/10.1145/3604915.3609492) 803 | - [Knowledge-based Multiple Adaptive Spaces Fusion for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608787) 804 | - [KGTORe: Tailored Recommendations through Knowledge-aware GNN Models](https://dl.acm.org/doi/pdf/10.1145/3604915.3608804) 805 | - [Pairwise Intent Graph Embedding Learning for Context-Aware Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608815) 806 | - [Heterogeneous Knowledge Fusion: A Novel Approach for Personalized Recommendation via LLM](https://dl.acm.org/doi/pdf/10.1145/3604915.3608874) 807 | - [STAN: Stage-Adaptive Network for Multi-Task Recommendation by Learning User Lifecycle-Based Representation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608796) 808 | - [Disentangling Motives behind Item Consumption and Social Connection for Mutually-enhanced Joint Prediction](https://dl.acm.org/doi/pdf/10.1145/3604915.3608767) 809 | - [BVAE: Behavior-aware Variational Autoencoder for Multi-Behavior Multi-Task Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608781) 810 | - [MCM: A Multi-task Pre-trained Customer Model for Personalization](https://dl.acm.org/doi/pdf/10.1145/3604915.3608868) 811 | - [How Should We Measure Filter Bubbles? A Regression Model and Evidence for Online News](https://dl.acm.org/doi/pdf/10.1145/3604915.3608805) 812 | - [Everyone’s a Winner! On Hyperparameter Tuning of Recommendation Models](https://dl.acm.org/doi/pdf/10.1145/3604915.3609488) 813 | - [What We Evaluate When We Evaluate Recommender Systems: Understanding Recommender Systems’ Performance using Item Response Theory](https://dl.acm.org/doi/pdf/10.1145/3604915.3608809) 814 | - [Identifying Controversial Pairs in Item-to-Item Recommendations](https://dl.acm.org/doi/pdf/10.1145/3604915.3608871) 815 | - [A Probabilistic Position Bias Model for Short-Video Recommendation Feeds](https://dl.acm.org/doi/pdf/10.1145/3604915.3608777) 816 | - [ADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction](https://dl.acm.org/doi/pdf/10.1145/3604915.3608813) 817 | - [Using Learnable Physics for Real-Time Exercise Form Recommendations](https://dl.acm.org/doi/pdf/10.1145/3604915.3608816) 818 | - [ReCon: Reducing Congestion in Job Recommendation using Optimal Transport](https://dl.acm.org/doi/pdf/10.1145/3604915.3608817) 819 | - [Interpretable User Retention Modeling in Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608818) 820 | - [Analysis Operations for Constraint-based Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3604915.3608819) 821 | - [Bootstrapped Personalized Popularity for Cold Start Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3604915.3608820) 822 | - [Beyond the Sequence: Statistics-Driven Pre-training for Stabilizing Sequential Recommendation Model](https://dl.acm.org/doi/pdf/10.1145/3604915.3608821) 823 | - [Personalized Category Frequency prediction for Buy It Again recommendations](https://dl.acm.org/doi/pdf/10.1145/3604915.3608822) 824 | - [Generative Next-Basket Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608823) 825 | - [Adversarial Sleeping Bandit Problems with Multiple Plays: Algorithm and Ranking Application](https://dl.acm.org/doi/pdf/10.1145/3604915.3608824) 826 | - [Collaborative filtering algorithms are prone to mainstream-taste bias](https://dl.acm.org/doi/pdf/10.1145/3604915.3608825) 827 | - [Hessian-aware Quantized Node Embeddings for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608826) 828 | - [Scalable Approximate NonSymmetric Autoencoder for Collaborative Filtering](https://dl.acm.org/doi/pdf/10.1145/3604915.3608827) 829 | - [M3REC: A Meta-based Multi-scenario Multi-task Recommendation Framework](https://dl.acm.org/doi/pdf/10.1145/3604915.3608828) 830 | - [Large Language Model Augmented Narrative Driven Recommendations](https://dl.acm.org/doi/pdf/10.1145/3604915.3608829) 831 | - [Incorporating Time in Sequential Recommendation Models](https://dl.acm.org/doi/pdf/10.1145/3604915.3608830) 832 | - [Enhancing Transformers without Self-supervised Learning: A Loss Landscape Perspective in Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608831) 833 | - [Adaptive Collaborative Filtering with Personalized Time Decay Functions for Financial Product Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608832) 834 | - [Private Matrix Factorization with Public Item Features](https://dl.acm.org/doi/pdf/10.1145/3604915.3608833) 835 | - [Deliberative Diversity for News Recommendations: Operationalization and Experimental User Study](https://dl.acm.org/doi/pdf/10.1145/3604915.3608834) 836 | - [Co-occurrence Embedding Enhancement for Long-tail Problem in Multi-Interest Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608835) 837 | - [Extended Conversion: Capturing Successful Interactions in Voice Shopping](https://dl.acm.org/doi/pdf/10.1145/3604915.3608836) 838 | - [On the Consistency of Average Embeddings for Item Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608837) 839 | - [Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608838) 840 | - [Widespread Flaws in Offline Evaluation of Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3604915.3608839) 841 | - [Towards Sustainability-aware Recommender Systems: Analyzing the Trade-off Between Algorithms Performance and Carbon Footprint](https://dl.acm.org/doi/pdf/10.1145/3604915.3608840) 842 | - [Group Fairness for Content Creators: the Role of Human and Algorithmic Biases under Popularity-based Recommendations](https://dl.acm.org/doi/pdf/10.1145/3604915.3608841) 843 | - [Providing Previously Unseen Users Fair Recommendations Using Variational Autoencoders](https://dl.acm.org/doi/pdf/10.1145/3604915.3608842) 844 | - [Scalable Deep Q-Learning for Session-Based Slate Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608843) 845 | - [CR-SoRec: BERT driven Consistency Regularization for Social Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608844) 846 | - [Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences](https://dl.acm.org/doi/pdf/10.1145/3604915.3608845) 847 | - [Interface Design to Mitigate Inflation in Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3604915.3608846) 848 | - [Towards Self-Explaining Sequence-Aware Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608847) 849 | - [Looks Can Be Deceiving: Linking User-Item Interactions and User’s Propensity Towards Multi-Objective Recommendations](https://dl.acm.org/doi/pdf/10.1145/3604915.3608848) 850 | - [Ti-DC-GNN: Incorporating Time-Interval Dual Graphs for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3604915.3608849) 851 | - [Of Spiky SVDs and Music Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608850) 852 | - [Topic-Level Bayesian Surprise and Serendipity for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3604915.3608851) 853 | - [Progressive Horizon Learning: Adaptive Long Term Optimization for Personalized Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608852) 854 | - [Stability of Explainable Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608853) 855 | - [Optimizing Long-term Value for Auction-Based Recommender Systems via On-Policy Reinforcement Learning](https://dl.acm.org/doi/pdf/10.1145/3604915.3608854) 856 | - [Deep Exploration for Recommendation Systems](https://dl.acm.org/doi/pdf/10.1145/3604915.3608855) 857 | - [Ex2Vec: Characterizing Users and Items from the Mere Exposure Effect](https://dl.acm.org/doi/pdf/10.1145/3604915.3608856) 858 | - [Initiative transfer in conversational recommender systems](https://dl.acm.org/doi/pdf/10.1145/3604915.3608858) 859 | - [Time-Aware Item Weighting for the Next Basket Recommendations](https://dl.acm.org/doi/pdf/10.1145/3604915.3608859) 860 | - [Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608860) 861 | - [Multiple Connectivity Views for Session-based Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608861) 862 | - [TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3608857) 863 | - [An Industrial Framework for Personalized Serendipitous Recommendation in E-commerce](https://dl.acm.org/doi/pdf/10.1145/3604915.3610234) 864 | - [RecQR: Using Recommendation Systems for Query Reformulation to correct unseen errors in spoken dialog systems](https://dl.acm.org/doi/pdf/10.1145/3604915.3610235) 865 | - [Scaling Session-Based Transformer Recommendations using Optimized Negative Sampling and Loss Functions](https://dl.acm.org/doi/pdf/10.1145/3604915.3610236) 866 | - [Visual Representation for Capturing Creator Theme in Brand-Creator Marketplace](https://dl.acm.org/doi/pdf/10.1145/3604915.3610237) 867 | - [Station and Track Attribute-Aware Music Personalization](https://dl.acm.org/doi/pdf/10.1145/3604915.3610239) 868 | - [Optimizing Podcast Discovery: Unveiling Amazon Music’s Retrieval and Ranking Framework](https://dl.acm.org/doi/pdf/10.1145/3604915.3610240) 869 | - [Towards Companion Recommenders Assisting Users’ Long-Term Journeys](https://dl.acm.org/doi/pdf/10.1145/3604915.3610241) 870 | - [Delivery Hero Recommendation Dataset: A Novel Dataset for Benchmarking Recommendation Algorithms](https://dl.acm.org/doi/pdf/10.1145/3604915.3610242) 871 | - [Transparently Serving the Public: Enhancing Public Service Media Values through Exploration](https://dl.acm.org/doi/pdf/10.1145/3604915.3610243) 872 | - [Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders](https://dl.acm.org/doi/pdf/10.1145/3604915.3610244) 873 | - [Nonlinear Bandits Exploration for Recommendations](https://dl.acm.org/doi/pdf/10.1145/3604915.3610245) 874 | - [Navigating the Feedback Loop in Recommender Systems: Insights and Strategies from Industry Practice](https://dl.acm.org/doi/pdf/10.1145/3604915.3610246) 875 | - [Leveling Up the Peloton Homescreen: A System and Algorithm for Dynamic Row Ranking](https://dl.acm.org/doi/pdf/10.1145/3604915.3610247) 876 | - [Creating the next generation of news experience on ekstrabladet.dk with recommender systems](https://dl.acm.org/doi/pdf/10.1145/3604915.3610248) 877 | - [From Research to Production: Towards Scalable and Sustainable Neural Recommendation Models on Commodity CPU Hardware](https://dl.acm.org/doi/pdf/10.1145/3604915.3610249) 878 | - [Unleash the Power of Context: Enhancing Large-Scale Recommender Systems with Context-Based Prediction Models](https://dl.acm.org/doi/pdf/10.1145/3604915.3610250) 879 | - [OutRank: Speeding up AutoML-based Model Search for Large Sparse Data sets with Cardinality-aware Feature Ranking](https://dl.acm.org/doi/pdf/10.1145/3604915.3610636) 880 | - [Evaluating The Effects of Calibrated Popularity Bias Mitigation: A Field Study](https://dl.acm.org/doi/pdf/10.1145/3604915.3610637) 881 | - [How Users Ride the Carousel: Exploring the Design of Multi-List Recommender Interfaces From a User Perspective](https://dl.acm.org/doi/pdf/10.1145/3604915.3610638) 882 | - [Leveraging Large Language Models for Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3610639) 883 | - [Integrating Offline Reinforcement Learning with Transformers for Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3610641) 884 | - [Learning the True Objectives of Multiple Tasks in Sequential Behavior Modeling](https://dl.acm.org/doi/pdf/10.1145/3604915.3610642) 885 | - [Integrating Item Relevance in Training Loss for Sequential Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3604915.3610643) 886 | - [Turning Dross Into Gold Loss: is BERT4Rec really better than SASRec?](https://dl.acm.org/doi/pdf/10.1145/3604915.3610644) 887 | - [Uncovering ChatGPT’s Capabilities in Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3604915.3610646) 888 | - [Continual Collaborative Filtering Through Gradient Alignment](https://dl.acm.org/doi/pdf/10.1145/3604915.3610648) 889 | - [Broadening the Scope: Evaluating the Potential of Recommender Systems beyond prioritizing Accuracy](https://dl.acm.org/doi/pdf/10.1145/3604915.3610649) 890 | - [Analyzing Accuracy versus Diversity in a Health Recommender System for Physical Activities: a Longitudinal User Study](https://dl.acm.org/doi/pdf/10.1145/3604915.3610650) 891 | - [On the Consistency, Discriminative Power and Robustness of Sampled Metrics in Offline Top-N Recommender System Evaluation](https://dl.acm.org/doi/pdf/10.1145/3604915.3610651) 892 | - [Climbing crags repetitive choices and recommendations](https://dl.acm.org/doi/pdf/10.1145/3604915.3610652) 893 | - [Improving Group Recommendations using Personality, Dynamic Clustering and Multi-Agent MicroServices](https://dl.acm.org/doi/pdf/10.1145/3604915.3610653) 894 | - [Uncertainty-adjusted Inductive Matrix Completion with Graph Neural Networks](https://dl.acm.org/doi/pdf/10.1145/3604915.3610654) 895 | - [An Exploration of Sentence-Pair Classification for Algorithmic Recruiting](https://dl.acm.org/doi/pdf/10.1145/3604915.3610657) 896 | - [Power Loss Function in Neural Networks for Predicting Click-Through Rate](https://dl.acm.org/doi/pdf/10.1145/3604915.3610658) 897 | - [Towards Health-Aware Fairness in Food Recipe Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3610659) 898 | - [A Model-Agnostic Framework for Recommendation via Interest-aware Item Embeddings](https://dl.acm.org/doi/pdf/10.1145/3604915.3610660) 899 | - [EasyStudy: Framework for Easy Deployment of User Studies on Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3604915.3610640) 900 | - [Localify.org: Locally-focus Music Artist and Event Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3610645) 901 | - [LLM Based Generation of Item-Description for Recommendation System](https://dl.acm.org/doi/pdf/10.1145/3604915.3610647) 902 | - [Re2Dan: Retrieval of Medical Documents for e-Health in Danish](https://dl.acm.org/doi/pdf/10.1145/3604915.3610655) 903 | - [Introducing LensKit-Auto, an Experimental Automated Recommender System (AutoRecSys) Toolkit](https://dl.acm.org/doi/pdf/10.1145/3604915.3610656) 904 | 905 | 906 | ## SIGIR 2023 907 | 908 | - [A Generic Learning Framework for Sequential Recommendation with Distribution Shifts](https://openreview.net/pdf?id=5vPaFVIrVLf) 909 | - [A Preference Learning Decoupling Framework for User Cold-Start Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539618.3591627) 910 | - [Adaptive Graph Representation Learning for Next POI Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539618.3591634) 911 | - [Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation](https://arxiv.org/pdf/2307.04571.pdf) 912 | - [Beyond the Overlapping Users: Cross-Domain Recommendation via Adaptive Anchor Link Learning](https://dl.acm.org/doi/pdf/10.1145/3539618.3591642) 913 | - [Beyond Two-Tower Matching: Learning Sparse Retrievable Cross-Interactions for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539618.3591643) 914 | - [Blurring-Sharpening Process Models for Collaborative Filtering](https://arxiv.org/pdf/2211.09324.pdf) 915 | - [Candidate–aware Graph Contrastive Learning for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539618.3591647) 916 | - [Causal Decision Transformer for Recommender Systems via Offline Reinforcement Learning](https://arxiv.org/pdf/2304.07920.pdf) 917 | - [Continuous Input Embedding Size Search For Recommender Systems](https://arxiv.org/pdf/2304.03501.pdf) 918 | - [Contrastive State Augmentations for Reinforcement Learning-Based Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3539618.3591656) 919 | - [Curse of “Low” Dimensionality in Recommender Systems](https://arxiv.org/pdf/2305.13597.pdf) 920 | - [Diffusion Recommender Model](https://arxiv.org/pdf/2304.04971.pdf) 921 | - [Distillation-Enhanced Graph Masked Autoencoders for Bundle Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539618.3591666) 922 | - [Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539618.3591672) 923 | - [Dynamic Graph Evolution Learning for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539618.3591674) 924 | - [Editable User Profiles for Controllable Text Recommendations](https://arxiv.org/pdf/2304.04250.pdf) 925 | - [EEDN: Enhanced Encoder-Decoder Network with Local and Global Context Learning for POI Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539618.3591678) 926 | - [Ensemble Modeling with Contrastive Knowledge Distillation for Sequential Recommendation](https://arxiv.org/pdf/2304.14668.pdf) 927 | - [Exploring scenarios of uncertainty about the users’ preferences in interactive recommendation systems](https://dl.acm.org/doi/pdf/10.1145/3539618.3591684) 928 | - [Fine-Grained Preference-Aware Personalized Federated POI Recommendation with Data Sparsity](https://dl.acm.org/doi/pdf/10.1145/3539618.3591688) 929 | - [Frequency Enhanced Hybrid Attention Network for Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539618.3591689) 930 | - [Generative-Contrastive Graph Learning for Recommendation](https://le-wu.com/files/Publications/CONFERENCES/SIGIR-23-yang.pdf) 931 | - [Graph Masked Autoencoder for Sequential Recommendation](https://arxiv.org/pdf/2305.04619.pdf) 932 | - [HDNR: A Hyperbolic-Based Debiased Approach for Personalized News Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539618.3591693) 933 | - [Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment](https://dl.acm.org/doi/pdf/10.1145/3539618.3591697) 934 | - [AutoTransfer: Instance Transfer for Cross-Domain Recommendations](https://dl.acm.org/doi/pdf/10.1145/3539618.3591701) 935 | - [Intent-aware Ranking Ensemble for Personalized Recommendation](https://arxiv.org/pdf/2304.07450.pdf) 936 | - [It’s Enough: Relaxing Diagonal Constraints in Linear Autoencoders for Recommendation](https://arxiv.org/pdf/2305.12922.pdf) 937 | - [Knowledge-enhanced Multi-View Graph Neural Networks for Session-based Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539618.3591706) 938 | - [Knowledge-refined Denoising Network for Robust Recommendation](https://arxiv.org/pdf/2304.14987.pdf) 939 | - [Learning Fine-grained User Interests for Micro-video Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539618.3591713) 940 | - [LightGT: A Light Graph Transformer for Multimedia Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539618.3591716) 941 | - [LinRec: Linear Attention Mechanism for Long-term Sequential Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3539618.3591717) 942 | - [LOAM: Improving Long-tail Session-based Recommendation via Niche Walk Augmentation and Tail Session Mixup](https://dl.acm.org/doi/pdf/10.1145/3539618.3591718) 943 | - [M2EU: Meta Learning for Cold-start Recommendation via Enhancing User Preference Estimation](https://dl.acm.org/doi/pdf/10.1145/3539618.3591719) 944 | - [M2GNN: Metapath and Multi-interest Aggregated Graph Neural Network for Tag-based Cross-domain Recommendation](https://arxiv.org/pdf/2304.07911.pdf) 945 | - [Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures](https://arxiv.org/pdf/2304.03054.pdf) 946 | - [Graph Transformer for Recommendation](https://arxiv.org/pdf/2306.02330.pdf) 947 | - [Measuring Item Global Residual Value for Fair Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539618.3591724) 948 | - [MELT: Mutual Enhancement of Long-Tailed User and Item for Sequential Recommendation](https://arxiv.org/pdf/2304.08382.pdf) 949 | - [Meta-optimized Contrastive Learning for Sequential Recommendation](https://arxiv.org/pdf/2304.07763.pdf) 950 | - [Mining Stable Preferences: Adaptive Modality Decorrelation for Multimedia Recommendation](https://arxiv.org/pdf/2306.14179.pdf) 951 | - [Mixed-Curvature Manifolds Interaction Learning for Knowledge Graph-aware Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539618.3591730) 952 | - [Aligning Distillation For Cold-start Item Recommendation](https://www4.comp.polyu.edu.hk/~xiaohuang/docs/Feiran_SIGIR2023.pdf) 953 | - [Model-Agnostic Decentralized Collaborative Learning for On-Device POI Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539618.3591733) 954 | - [Multi-behavior Self-supervised Learning for Recommendation](https://arxiv.org/pdf/2305.18238.pdf) 955 | - [Multi-view Hypergraph Contrastive Policy Learning for Conversational Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539618.3591737) 956 | - [Multi-View Multi-Aspect Neural Networks for Next-Basket Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539618.3591738) 957 | - [Multimodal Counterfactual Learning Network for Multimedia-based Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539618.3591739) 958 | - [News Popularity Beyond the Click-Through-Rate for Personalized Recommendations](https://dl.acm.org/doi/pdf/10.1145/3539618.3591741) 959 | - [Next Basket Recommendation with Intent-aware Hypergraph Adversarial Network](https://dl.acm.org/doi/pdf/10.1145/3539618.3591742) 960 | - [PLATE: A Prompt-Enhanced Paradigm for Multi-Scenario Recommendations](https://dl.acm.org/doi/pdf/10.1145/3539618.3591750) 961 | - [Poisoning Self-supervised Learning Based Sequential Recommendations](https://dl.acm.org/doi/pdf/10.1145/3539618.3591751) 962 | - [Prompt Learning for News Recommendation](https://arxiv.org/pdf/2304.05263.pdf) 963 | - [RCENR: A Reinforced and Contrastive Heterogeneous Network Reasoning Model for Explainable News Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539618.3591753) 964 | - [Rectifying Unfairness in Recommendation Feedback Loop](https://dl.acm.org/doi/pdf/10.1145/3539618.3591754) 965 | - [Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation](https://arxiv.org/pdf/2304.13643.pdf) 966 | - [Single-shot Feature Selection for Multi-task Recommendations](https://dl.acm.org/doi/pdf/10.1145/3539618.3591767) 967 | - [Spatio-Temporal Hypergraph Learning for Next POI Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539618.3591770) 968 | - [Strategy-aware Bundle Recommender System](https://dl.acm.org/doi/pdf/10.1145/3539618.3591771) 969 | - [Time-interval Aware Share Recommendation via Bi-directional Continuous Time Dynamic Graphs](https://dl.acm.org/doi/pdf/10.1145/3539618.3591775) 970 | - [Topic-enhanced Graph Neural Networks for Extraction-based Explainable Recommendation](https://dl.acm.org/doi/pdf/10.1145/3539618.3591776) 971 | - [Triple Structural Information Modelling for Accurate, Explainable and Interactive Recommendation](https://arxiv.org/pdf/2304.11528.pdf) 972 | - [When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?](https://arxiv.org/pdf/2305.01801.pdf) 973 | - [When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation](https://arxiv.org/pdf/2305.10822.pdf) 974 | - [Wisdom of Crowds and Fine-Grained Learning for Serendipity Recommendations](https://dl.acm.org/doi/pdf/10.1145/3539618.3591787) 975 | 976 | 977 | ## KDD 2023 978 | 979 | - [Generative Flow Network for Listwise Recommendation](https://dl.acm.org/doi/pdf/10.1145/3580305.3599364) 980 | - [A Sublinear Time Algorithm for Opinion Optimization in Directed Social Networks via Edge Recommendation](https://dl.acm.org/doi/pdf/10.1145/3580305.3599247) 981 | - [Text Is All You Need: Learning Language Representations for Sequential Recommendation](https://arxiv.org/pdf/2305.13731.pdf) 982 | - [MAP: A Model-agnostic Pretraining Framework for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3580305.3599422) 983 | - [Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3580305.3599277) 984 | - [PrefRec: Recommender Systems with Human Preferences for Reinforcing Long-Term User Engagement](https://personal.ntu.edu.sg/boan/papers/KDD23_PrefRec.pdf) 985 | - [Efficient Bi-Level Optimization for Recommendation Denoising](https://dl.acm.org/doi/pdf/10.1145/3580305.3599324) 986 | - [Adaptive Disentangled Transformer for Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3580305.3599253) 987 | - [Path-Specific Counterfactual Fairness for Recommender Systems](https://arxiv.org/pdf/2306.02615.pdf) 988 | - [Meta Graph Learning for Long-Tail Recommendation](https://dl.acm.org/doi/pdf/10.1145/3580305.3599428) 989 | - [Graph Neural Bandits](https://dl.acm.org/doi/pdf/10.1145/3580305.3599371) 990 | - [E-commerce Search via Content Collaborative Graph Neural Network](https://dl.acm.org/doi/pdf/10.1145/3580305.3599320) 991 | - [Criteria Tell You More than Ratings: Criteria Preference-Aware Light Graph Convolution for Effective Multi-Criteria Recommendation](https://arxiv.org/pdf/2305.18885.pdf) 992 | - [Knowledge Graph Self-Supervised Rationalization for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3580305.3599400) 993 | - [On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering](https://arxiv.org/pdf/2306.03624.pdf) 994 | - [Shilling Black-Box Review-Based Recommender Systems through Fake Review Generation](https://dl.acm.org/doi/pdf/10.1145/3580305.3599502) 995 | - [Improving Conversational Recommendation Systems via Counterfactual Data Simulation](https://arxiv.org/pdf/2306.02842.pdf) 996 | - [LATTE: A Framework for Learning Item-Features to Make a Domain-Expert for Effective Conversational Recommendation](https://dl.acm.org/doi/pdf/10.1145/3580305.3599401) 997 | - [User-Regulation Deconfounded Conversational Recommender System with Bandit Feedback](https://dl.acm.org/doi/pdf/10.1145/3580305.3599539) 998 | - [Hierarchical Invariant Learning for Domain Generalization Recommendation](https://dl.acm.org/doi/pdf/10.1145/3580305.3599377) 999 | - [UCEpic: Unifying Aspect Planning and Lexical Constraints for Generating Explanations in Recommendation](https://dl.acm.org/doi/pdf/10.1145/3580305.3599535) 1000 | - [Debiasing Recommendation by Learning Identifiable Latent Confounders](https://arxiv.org/pdf/2302.05052.pdf) 1001 | - [Reconsidering Learning Objectives in Unbiased Recommendation: A Distribution Shift Perspective](https://dl.acm.org/doi/pdf/10.1145/3580305.3599487) 1002 | - [Who Should Be Given Incentives? Counterfactual Optimal Treatment Regimes Learning for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3580305.3599550) 1003 | - [Meta Multi-Agent Exercise Recommendation: A Game Application Perspective](https://le-wu.com/files/Publications/CONFERENCES/KDD-23-liu.pdf) 1004 | 1005 | 1006 | ## CIKM 2023 1007 | 1008 | - [Multitask Ranking System for Immersive Feed and No More Clicks: A Case Study of Short-Form Video Recommendation](https://dl.acm.org/doi/pdf/10.1145/3583780.3615489) 1009 | - [An Unified Search and Recommendation Foundation Model for Cold-Start Scenario](https://arxiv.org/pdf/2309.08939.pdf) 1010 | - [Graph Exploration Matters: Improving both individual-level and system-level diversity in WeChat Feed Recommendation](https://arxiv.org/pdf/2306.00009.pdf) 1011 | - [Leveraging Watch-time Feedback for Short-Video Recommendations: A Causal Labeling Framework](https://arxiv.org/pdf/2306.17426.pdf) 1012 | - [3MN: Three Meta Networks for Multi-Scenario and Multi-Task Learning in Online Advertising Recommender Systems](https://dl.acm.org/doi/abs/10.1145/3583780.3614651) 1013 | - [SHARK: A Lightweight Model Compression Approach for Large-scale Recommender Systems](https://arxiv.org/pdf/2308.09395.pdf) 1014 | - [Popularity-aware Distributionally Robust Optimization for Recommendation System](https://dl.acm.org/doi/pdf/10.1145/3543507.3583223) 1015 | - [BOLT: An Automated Deep Learning Framework for Training and Deploying Large-Scale Search and Recommendation Models on Commodity CPU Hardware](https://arxiv.org/pdf/2303.17727.pdf) 1016 | - [An Incremental Update Framework for Online Recommenders with Data-Driven Prior](https://arxiv.org/pdf/2312.15903.pdf) 1017 | - [Dual Interests-Aligned Graph Auto-Encoders for Cross-domain Recommendation in WeChat](https://dl.acm.org/doi/abs/10.1145/3583780.3614676) 1018 | - [RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation](https://arxiv.org/pdf/2309.10469.pdf) 1019 | - [Multi-gate Mixture-of-Contrastive-Experts with Graph-based Gating Mechanism for TV Recommendation](https://dl.acm.org/doi/abs/10.1145/3583780.3615488) 1020 | - [Learning and Optimization of Implicit Negative Feedback for Industrial Short-video Recommender System](https://arxiv.org/pdf/2308.13249.pdf) 1021 | - [A Data-Driven Index Recommendation System for Slow Queries](https://dl.acm.org/doi/abs/10.1145/3583780.3614731) 1022 | - [Towards Improving Accuracy and Computation Cost Optimization of Recommendation Systems](https://dl.acm.org/doi/abs/10.1145/3583780.3616006) 1023 | - [Prod2Vec-Var: A Session Based Recommendation System with Enhanced Diversity](https://dl.acm.org/doi/abs/10.1145/3583780.3615995) 1024 | - [Vigil: Effective end-to-end monitoring for large-scale recommender systems at Glance](https://dl.acm.org/doi/abs/10.1145/3583780.3615997) 1025 | - [Timestamps as Prompts for Geography-Aware Location Recommendation](https://arxiv.org/pdf/2304.04151.pdf) 1026 | - [Improving Long-Tail Item Recommendation with Graph Augmentation](https://dl.acm.org/doi/abs/10.1145/3583780.3614929) 1027 | - [Contrastive Counterfactual Learning for Causality-aware Interpretable Recommender Systems](https://dl.acm.org/doi/abs/10.1145/3583780.3614823) 1028 | - [Zero-shot Item-based Recommendation via Multi-task Product Knowledge Graph Pre-Training](https://arxiv.org/pdf/2305.07633.pdf) 1029 | - [Text Matching Improves Sequential Recommendation by Reducing Popularity Biases](https://arxiv.org/pdf/2308.14029.pdf) 1030 | - [KG4Ex: An Explainable Knowledge Graph-Based Approach for Exercise Recommendation](https://dl.acm.org/doi/abs/10.1145/3583780.3614943) 1031 | - [CPMR: Context-Aware Incremental Sequential Recommendation with Pseudo-Multi-Task Learning](https://arxiv.org/pdf/2309.04802.pdf) 1032 | - [Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System](https://arxiv.org/pdf/2308.07760.pdf) 1033 | - [Federated News Recommendation with Fine-grained Interpolation and Dynamic Clustering](https://dl.acm.org/doi/abs/10.1145/3583780.3614881) 1034 | - [HyperBandit: Contextual Bandit with Hypernewtork for Time-Varying User Preferences in Streaming Recommendation](https://arxiv.org/pdf/2308.08497.pdf) 1035 | - [Modeling Sequential Collaborative User Behaviors For Seller-Aware Next Basket Recommendation](https://dl.acm.org/doi/abs/10.1145/3583780.3614973) 1036 | - [Sequential recommendation via an adaptive cross-domain knowledge decomposition](https://dl.acm.org/doi/abs/10.1145/3583780.3615058) 1037 | - [DPGN: Denoising Periodic Graph Network for Life Service Recommendation](https://dl.acm.org/doi/abs/10.1145/3583780.3614850) 1038 | - [Bi-channel Multiple Sparse Graph Attention Networks for Session-based Recommendation](https://dl.acm.org/doi/abs/10.1145/3583780.3614791) 1039 | - [Towards Communication-Efficient Model Updating for On-Device Session-Based Recommendation](https://arxiv.org/pdf/2308.12777.pdf) 1040 | - [Celebrity-aware Graph Contrastive Learning Framework for Social Recommendation](https://dl.acm.org/doi/abs/10.1145/3583780.3614806) 1041 | - [Disentangled Interest importance aware Knowledge Graph Neural Network for Fund Recommendation](https://dl.acm.org/doi/abs/10.1145/3583780.3614846) 1042 | - [Targeted Shilling Attacks on GNN-based Recommender Systems](https://dl.acm.org/doi/abs/10.1145/3583780.3615073) 1043 | - [Decentralized Graph Neural Network for Privacy-Preserving Recommendation](https://arxiv.org/pdf/2308.08072.pdf) 1044 | - [Multi-domain Recommendation with Embedding Disentangling and Domain Alignment](https://arxiv.org/pdf/2308.05508.pdf) 1045 | - [Diffusion Augmentation for Sequential Recommendation](https://arxiv.org/pdf/2309.12858.pdf) 1046 | - [Prompt Distillation for Efficient LLM-based Recommendation](https://lileipisces.github.io/files/CIKM23-POD-paper.pdf) 1047 | - [Adaptive Multi-Modalities Fusion in Sequential Recommendation Systems](https://arxiv.org/pdf/2308.15980.pdf) 1048 | - [Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph](https://arxiv.org/pdf/2308.07752.pdf) 1049 | - [iHAS: Instance-wise Hierarchical Architecture Search for Deep Learning Recommendation Models](https://arxiv.org/pdf/2309.07967.pdf) 1050 | - [Causality-guided Graph Learning for Session-based Recommendation](https://www.researchgate.net/profile/Dianer-Yu/publication/373143453_Causality-guided_Graph_Learning_for_Session-based_Recommendation/links/652b3fe006bdd619c48fdd00/Causality-guided-Graph-Learning-for-Session-based-Recommendation.pdf) 1051 | - [Dual-Process Graph Neural Network for Diversified Recommendation](https://dl.acm.org/doi/abs/10.1145/3583780.3614853) 1052 | - [Modeling Preference as Weighted Distribution over Functions for User Cold-start Recommendation](https://dl.acm.org/doi/abs/10.1145/3583780.3614972) 1053 | - [Single-User Injection for Invisible Shilling Attack against Recommender Systems](https://arxiv.org/pdf/2308.10467.pdf) 1054 | - [Capturing Popularity Trends: A Simplistic Non-Personalized Approach for Enhanced Item Recommendation](https://arxiv.org/pdf/2308.08799.pdf) 1055 | - [Batch-Mix Negative Sampling for Learning Recommendation Retrievers](https://zheng-kai.com/paper/cikm_2023_fan.pdf) 1056 | - [Post-hoc Selection of Pareto-Optimal Solutions in Search and Recommendation](https://arxiv.org/pdf/2306.12165.pdf) 1057 | - [SMEF: Social-aware Multi-dimensional Edge Features-based Graph Representation Learning for Recommendation](https://dl.acm.org/doi/abs/10.1145/3583780.3615063) 1058 | - [Robust Basket Recommendation via Noise-tolerated Graph Contrastive Learning](https://weitianxin.github.io/files/CIKM23.pdf) 1059 | - [How Expressive are Graph Neural Networks in Recommendation?](https://arxiv.org/pdf/2308.11127.pdf) 1060 | - [Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start Recommendation](https://arxiv.org/pdf/2302.14640.pdf) 1061 | - [Multi-modal Mixture of Experts Represetation Learning for Sequential Recommendation](https://dl.acm.org/doi/abs/10.1145/3583780.3614978) 1062 | - [Parallel Knowledge Enhancement based Framework for Multi-behavior Recommendation](https://arxiv.org/pdf/2308.04807.pdf) 1063 | - [Learning the Co-evolution Process on Live Stream Platforms with Dual Self-attention for Next-topic Recommendations](https://dl.acm.org/doi/abs/10.1145/3583780.3614952) 1064 | - [Graph Enhanced Hierarchical Reinforcement Learning for Goal-oriented Learning Path Recommendation](https://dl.acm.org/doi/abs/10.1145/3583780.3614897) 1065 | - [Periodicity May Be Emanative: Hierarchical Contrastive Learning for Sequential Recommendation](https://dl.acm.org/doi/abs/10.1145/3583780.3615007) 1066 | - [TPUF: Enhancing Cross-domain Sequential Recommendation via Transferring Pre-trained User Features](https://dl.acm.org/doi/abs/10.1145/3583780.3615094) 1067 | - [Cracking the Code of Negative Transfer: A Cooperative Game Theoretic Approach for Cross-Domain Sequential Recommendation](https://arxiv.org/pdf/2311.13188.pdf) 1068 | - [Self-supervised Contrastive Enhancement with Symmetric Few-shot Learning Towers for Cold-start News Recommendation](https://dl.acm.org/doi/abs/10.1145/3583780.3615053) 1069 | - [Knowledge-Aware Cross-Semantic Alignment for Domain-Level Zero-Shot Recommendation](https://dl.acm.org/doi/abs/10.1145/3583780.3614945) 1070 | - [Dual-Oriented Contrast for Recommendation with A Stop-Gradient Operation](https://dl.acm.org/doi/pdf/10.1145/3583780.3614852) 1071 | - [CDR: Conservative Doubly Robust Learning for Debiased Recommendation](https://arxiv.org/pdf/2308.08461.pdf) 1072 | - [Enhancing Repeat-Aware Recommendation from a Temporal-Sequential Perspective](https://dl.acm.org/doi/abs/10.1145/3583780.3614866) 1073 | - [Dual-view Contrastive Learning for Auction Recommendation](https://dl.acm.org/doi/abs/10.1145/3583780.3614854) 1074 | - [Deep Task-specific Bottom Representation Network for Multi-Task Recommendation](https://arxiv.org/pdf/2308.05996.pdf) 1075 | - [AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential Recommendation](https://arxiv.org/pdf/2205.08776.pdf) 1076 | - [Quad-Tier Entity Fusion Contrastive Representation Learning for Knowledge Aware Recommendation System](https://dl.acm.org/doi/pdf/10.1145/3583780.3615020) 1077 | - [Attention Calibration for Transformer-based Sequential Recommendation](https://arxiv.org/pdf/2308.09419.pdf) 1078 | - [APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation](https://arxiv.org/pdf/2311.02816.pdf) 1079 | - [CLSPRec: Contrastive Learning of Long and Short-term Preferences for Next POI Recommendation](https://dl.acm.org/doi/abs/10.1145/3583780.3614813) 1080 | - [HAMUR: Hyper Adapter for Multi-Domain Recommendation](https://arxiv.org/pdf/2309.06217.pdf) 1081 | - [Scalable Neural Contextual Bandit for Recommender Systems](https://arxiv.org/pdf/2306.14834.pdf) 1082 | - [Large Language Models as Zero-Shot Conversational Recommenders](https://arxiv.org/pdf/2308.10053.pdf) 1083 | - [Task-Difficulty-Aware Meta-Learning with Adaptive Update Strategies for User Cold-Start Recommendation](https://dl.acm.org/doi/abs/10.1145/3583780.3615074) 1084 | - [AutoSeqRec: Autoencoder for Efficient Sequential Recommendation](https://arxiv.org/pdf/2308.06878.pdf) 1085 | - [Diversity-aware Deep Ranking Network for Recommendation](https://dl.acm.org/doi/abs/10.1145/3583780.3614848) 1086 | - [Multimodal Optimal Transport Knowledge Distillation for Cross-domain Recommendation](https://dl.acm.org/doi/abs/10.1145/3583780.3614983) 1087 | - [A two-tier shared embedding method for review-based recommender systems](https://dl.acm.org/doi/pdf/10.1145/3583780.3614770) 1088 | - [MUSE: Music Recommender System with Shuffle Play Recommendation Enhancement](https://arxiv.org/pdf/2308.09649.pdf) 1089 | - [KuaiSAR: A Unified Search And Recommendation Dataset](https://arxiv.org/pdf/2306.07705.pdf) 1090 | - [Multi-Granularity Attention Model for Group Recommendation](https://arxiv.org/pdf/2308.04017.pdf) 1091 | - [Counterfactual Adversarial Learning for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3583780.3615152) 1092 | - [Counterfactual Graph Augmentation for Consumer Unfairness Mitigation in Recommender Systems](https://arxiv.org/pdf/2308.12083.pdf) 1093 | - [RecRec: Algorithmic Recourse for Recommender Systems](https://arxiv.org/pdf/2308.14916.pdf) 1094 | - [Graph-based Alignment and Uniformity for Recommendation](https://arxiv.org/pdf/2308.09292.pdf) 1095 | - [STGIN: Spatial-Temporal Graph Interaction Network for large-scale POI recommendation](https://arxiv.org/pdf/2309.02251.pdf) 1096 | - [A Flash Attention Transformer for Multi-Behaviour Recommendation](https://dl.acm.org/doi/pdf/10.1145/3583780.3615206) 1097 | - [G-Meta: Distributed Meta Learning in GPU Clusters for Large-Scale Recommender System](https://dl.acm.org/doi/abs/10.1145/3583780.3615208) 1098 | - [DPAN: Dynamic Preference-based and Attribute-aware Network for Relevant Recommendations](https://arxiv.org/pdf/2308.10527.pdf) 1099 | - [MI-DPG: Decomposable Parameter Generation Network Based on Mutual Information for Multi-Scenario Recommendation](https://dl.acm.org/doi/abs/10.1145/3583780.3615223) 1100 | - [Efficient Multi-Task Learning via Generalist Recommender](https://dl.acm.org/doi/abs/10.1145/3583780.3615229) 1101 | - [BI-GCN: Bilateral Interactive Graph Convolutional Network for Recommendation](https://dl.acm.org/doi/abs/10.1145/3583780.3615232) 1102 | - [MvFS: Multi-view Feature Selection for Recommender System](https://arxiv.org/pdf/2309.02064.pdf) 1103 | - [SeqGen: A Sequence Generator via User Side Information for Behavior Sparsity in Recommendation](https://dl.acm.org/doi/abs/10.1145/3583780.3615244) 1104 | - [Attribute-enhanced Dual Channel Representation Learning for Session-based Recommendation](https://dl.acm.org/doi/abs/10.1145/3583780.3615245) 1105 | - [Incorporating Co-purchase Correlation for Next-basket Recommendation](https://dl.acm.org/doi/abs/10.1145/3583780.3615257) 1106 | - [MTKDN: Multi-Task Knowledge Disentanglement Network for Recommendation](https://dl.acm.org/doi/abs/10.1145/3583780.3615271) 1107 | - [TCCM: Time and Content-aware Causal Model for Unbiased News Recommendation](https://www.researchgate.net/profile/Yewang-Chen/publication/374907674_TCCM_Time_and_Content-Aware_Causal_Model_for_Unbiased_News_Recommendation/links/653b46451d6e8a707050849f/TCCM-Time-and-Content-Aware-Causal-Model-for-Unbiased-News-Recommendation.pdf) 1108 | - [Personalized Interest Sustainability Modeling for Sequential POI Recommendation](https://dl.acm.org/doi/abs/10.1145/3583780.3615278) 1109 | - [Boosting Meta-learning Cold-start Recommendation with Graph Neural Network](https://dl.acm.org/doi/abs/10.1145/3583780.3615283) 1110 | - [Modeling Sequential Collaborative User Behaviors For Seller-Aware Next Basket Recommendation](https://dl.acm.org/doi/abs/10.1145/3583780.3614973) 1111 | - [PopDCL: Popularity-aware Debiased Contrastive Loss for Collaborative Filtering](https://dl.acm.org/doi/abs/10.1145/3583780.3615009) 1112 | - [Retrieving GNN Architecture for Collaborative Filtering](http://www.shichuan.org/doc/155.pdf) 1113 | - [Toward a Better Understanding of Loss Functions for Collaborative Filtering](https://arxiv.org/pdf/2308.06091.pdf) 1114 | - [Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering](https://arxiv.org/pdf/2306.15905.pdf) 1115 | - [Geometry Interaction Augmented Graph Collaborative Filtering](https://dl.acm.org/doi/abs/10.1145/3583780.3615204) 1116 | 1117 | 1118 | ## IJCAI 2023 1119 | 1120 | - [Self-supervised Graph Disentangled Networks for Review-based Recommendation](https://dl.acm.org/doi/abs/10.24963/ijcai.2023/254) 1121 | - [Towards Hierarchical Policy Learning for Conversational Recommendation with Hypergraph-based Reinforcement Learning](https://arxiv.org/pdf/2305.02575.pdf) 1122 | - [Sequential Recommendation with Probabilistic Logical Reasoning](https://arxiv.org/pdf/2304.11383.pdf) 1123 | - [Federated Probabilistic Preference Distribution Modelling with Compactness Co-Clustering for Privacy-Preserving Multi-Domain Recommendation](https://www.ijcai.org/proceedings/2023/0245.pdf) 1124 | - [Basket Representation Learning by Hypergraph Convolution on Repeated Items for Next-basket Recommendation](https://www.ijcai.org/proceedings/2023/0268.pdf) 1125 | - [Probabilistic Masked Attention Networks for Explainable Sequential Recommendation](https://www.ijcai.org/proceedings/2023/0230.pdf) 1126 | - [Dual Personalization on Federated Recommendation](https://arxiv.org/pdf/2301.08143.pdf) 1127 | - [Curriculum Multi-Level Learning for Imbalanced Live-Stream Recommendation](https://dl.acm.org/doi/abs/10.24963/ijcai.2023/267) 1128 | - [Discriminative-Invariant Representation Learning for Unbiased Recommendation](https://jiawei-chen.github.io/paper/DIRL.pdf) 1129 | - [Denoised Self-Augmented Learning for Social Recommendation](https://arxiv.org/pdf/2305.12685.pdf) 1130 | - [Intent-aware Recommendation via Disentangled Graph Contrastive Learning](https://www.ijcai.org/proceedings/2023/0260.pdf) 1131 | 1132 | 1133 | ## ICML 2023 1134 | 1135 | - [Vertical Federated Graph Neural Network for Recommender System](https://proceedings.mlr.press/v202/mai23b/mai23b.pdf) 1136 | - [Propensity Matters: Measuring and Enhancing Balancing for Recommendation](https://proceedings.mlr.press/v202/li23ah/li23ah.pdf) 1137 | - [Performative Recommendation: Diversifying Content via Strategic Incentives](https://proceedings.mlr.press/v202/eilat23a/eilat23a.pdf) 1138 | - [Curriculum Co-disentangled Representation Learning across Multiple Environments for Social Recommendation](https://proceedings.mlr.press/v202/wang23z/wang23z.pdf) 1139 | 1140 | 1141 | ## NeurIPS 2023 1142 | 1143 | 1144 | - [Mitigating the Popularity Bias of Graph Collaborative Filtering: A Dimensional Collapse Perspective](https://openreview.net/attachment?id=MvCq52yt9Y&name=pdf) 1145 | - [Blocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraints](https://openreview.net/attachment?id=Ntd6X7uWYF&name=pdf) 1146 | - [Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss](https://openreview.net/attachment?id=bNNIf8F9OU&name=pdf) 1147 | - [Recommender Systems with Generative Retrieval](https://openreview.net/attachment?id=BJ0fQUU32w&name=pdf) 1148 | - [Lending Interaction Wings to Recommender Systems with Conversational Agents](https://openreview.net/attachment?id=x7q7w07r6Y&name=pdf) 1149 | - [Supply-Side Equilibria in Recommender Systems](https://openreview.net/attachment?id=eqyhjLG5Nr&name=pdf) 1150 | - [Fairly Recommending with Social Attributes: A Flexible and Controllable Optimization Approach](https://openreview.net/attachment?id=NP5xb00Y6a&name=pdf) 1151 | - [Rethinking Incentives in Recommender Systems: Are Monotone Rewards Always Beneficial?](https://openreview.net/attachment?id=xUyBP16Q5J&name=pdf) 1152 | - [Theoretically Guaranteed Bidirectional Data Rectification for Robust Sequential Recommendation](https://openreview.net/attachment?id=mHsxsrLl0y&name=pdf) 1153 | - [Multi-Objective Intrinsic Reward Learning for Conversational Recommender Systems](https://openreview.net/attachment?id=qYAp31KwU2&name=pdf) 1154 | - [UltraRE: Enhancing RecEraser for Recommendation Unlearning via Error Decomposition](https://openreview.net/attachment?id=93NLxUojvc&name=pdf) 1155 | - [Removing Hidden Confounding in Recommendation: A Unified Multi-Task Learning Approach](https://openreview.net/attachment?id=4IWJZjbRFj&name=pdf) 1156 | - [Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion](https://openreview.net/attachment?id=oFpBnt6bgC&name=pdf) 1157 | - [REFINE: A Fine-Grained Medication Recommendation System Using Deep Learning and Personalized Drug Interaction Modeling](https://openreview.net/attachment?id=GsCTjmYe5v&name=pdf) 1158 | - [On the Relationship Between Relevance and Conflict in Online Social Link Recommendations](https://openreview.net/attachment?id=CrpL8mGa0Q&name=pdf) 1159 | - [Enhancing User Intent Capture in Session-Based Recommendation with Attribute Patterns](https://openreview.net/attachment?id=AV3iZlDrzF&name=pdf) 1160 | - [Cascading Bandits: Optimizing Recommendation Frequency in Delayed Feedback Environments](https://openreview.net/attachment?id=LClyG4vZmS&name=pdf) 1161 | 1162 | 1163 | 1164 | # 2022 1165 | 1166 | ## SIGIR 2022 1167 | 1168 | - [Mitigating the Filter Bubble While Maintaining Relevance: Targeted Diversification with VAE-based Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3477495.3531890) 1169 | - [Decoupled Side Information Fusion for Sequential Recommendation](https://arxiv.org/pdf/2204.11046.pdf) 1170 | - [Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation](https://bhooi.github.io/papers/hmt_sigir22.pdf) 1171 | - [Interpolative Distillation for Unifying Biased and Debiased Recommendation](https://web.archive.org/web/20220711205133id_/https://dl.acm.org/doi/pdf/10.1145/3477495.3532002) 1172 | - [Locality-Sensitive State-Guided Experience Replay Optimization for Sparse-Reward in Online Recommendation](https://cseweb.ucsd.edu/~jmcauley/pdfs/sigir22b.pdf) 1173 | - [Unify Local and Global Information for Top-N Recommendation](https://dl.acm.org/doi/pdf/10.1145/3477495.3532070?casa_token=7E2av5uAJSoAAAAA:xeCcuNyXZ94_gP1AaGZRkLSEE2vsyTXYq_9D1oUDYZ3e2nh5ZwBtQBLDC3_QjdBI9U-2T_82tVBo7Q) 1174 | - [Co-training Disentangled Domain Adaptation Network for Leveraging Popularity Bias in Recommenders](https://web.archive.org/web/20220709074001id_/https://dl.acm.org/doi/pdf/10.1145/3477495.3531952) 1175 | - [User-Aware Multi-Interest Learning for Candidate Matching in Recommenders](https://dl.acm.org/doi/pdf/10.1145/3477495.3532073) 1176 | - [Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations](https://arxiv.org/pdf/2204.11241.pdf) 1177 | - [Multi-Level Interaction Reranking with User Behavior History](https://arxiv.org/pdf/2204.09370.pdf) 1178 | - [User-controllable Recommendation Against Filter Bubbles](https://arxiv.org/pdf/2204.13844.pdf) 1179 | - [DisenCDR: Learning Disentangled Representations for Cross-Domain Recommendation](https://dl.acm.org/doi/pdf/10.1145/3477495.3531967) 1180 | - [Thinking inside The Box: Learning Hypercube Representations for Group Recommendation](https://arxiv.org/pdf/2204.02592.pdf) 1181 | - [A Review-aware Graph Contrastive Learning Framework for Recommendation](https://arxiv.org/pdf/2204.12063.pdf) 1182 | - [Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3477495.3531937) 1183 | - [On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation](https://dl.acm.org/doi/pdf/10.1145/3477495.3531775) 1184 | - [Multi-Agent RL-based Information Selection Model for Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3477495.3532022) 1185 | - [Knowledge Graph Contrastive Learning for Recommendation](https://arxiv.org/pdf/2205.00976.pdf) 1186 | - [Enhancing CTR Prediction with Context-Aware Feature Representation Learning](https://arxiv.org/pdf/2204.08758.pdf) 1187 | - [Joint Multisided Exposure Fairness for Recommendation](https://arxiv.org/pdf/2205.00048.pdf) 1188 | - [When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3477495.3532081) 1189 | - [Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective](https://dl.acm.org/doi/pdf/10.1145/3477495.3531714) 1190 | - [Single-shot Embedding Dimension Search in Recommender System](https://arxiv.org/pdf/2204.03281.pdf) 1191 | - [Learning to Infer User Implicit Preference in Conversational Recommendation](https://dl.acm.org/doi/pdf/10.1145/3477495.3531844) 1192 | - [Doubly-Adaptive Reinforcement Learning for Cross-Domain Interactive Recommendation](https://dl.acm.org/doi/pdf/10.1145/3477495.3531969) 1193 | - [An Attribute-Driven Mirroring Graph Network for Session-based Recommendation](https://dl.acm.org/doi/pdf/10.1145/3477495.3531935) 1194 | - [Geometric Disentangled Collaborative Filtering](http://www.shichuan.org/doc/136.pdf) 1195 | - [Hypergraph Contrastive Collaborative Filtering](https://dl.acm.org/doi/pdf/10.1145/3477495.3532058) 1196 | - [Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System](https://arxiv.org/pdf/2204.08807.pdf) 1197 | - [User-Centric Conversational Recommendation with Multi-Aspect User Modeling](https://arxiv.org/pdf/2204.09263.pdf) 1198 | - [MGPolicy: Meta Graph Enhanced Off-policy Learning for Recommendations](https://dl.acm.org/doi/pdf/10.1145/3477495.3532021) 1199 | - [HIEN: Hierarchical Intention Embedding Network for Click-Through Rate Prediction](https://arxiv.org/pdf/2206.00510.pdf) 1200 | - [Webformer: Pre-training with Web Pages for Information Retrieval](https://dl.acm.org/doi/pdf/10.1145/3477495.3532086) 1201 | - [Forest-based Deep Recommender](https://dl.acm.org/doi/pdf/10.1145/3477495.3531980) 1202 | - [Bilateral Self-unbiased Recommender Learning from Biased Implicit Feedback](http://www.joonseok.net/papers/biser.pdf) 1203 | - [Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation](https://arxiv.org/pdf/2205.04181.pdf) 1204 | - [Privacy-Preserving Synthetic Data Generation for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3477495.3532044) 1205 | - [Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering](https://arxiv.org/pdf/2204.12326.pdf) 1206 | - [DAWAR: Diversity-aware Web APIs Recommendation for Mashup Creation based on Correlation Graph](https://dl.acm.org/doi/pdf/10.1145/3477495.3531962) 1207 | - [Variational Reasoning about User Preferences for Conversational Recommendation](https://staff.fnwi.uva.nl/m.derijke/wp-content/papercite-data/pdf/ren-2022-variational.pdf) 1208 | - [Alleviating Spurious Correlations in Knowledge-aware Recommendations through Counterfactual Generator](https://dl.acm.org/doi/pdf/10.1145/3477495.3531934) 1209 | - [NAS-CTR: Efficient Neural Architecture Search for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3477495.3532030) 1210 | - [Exploiting Variational Domain-Invariant User Embedding for Partially Overlapped Cross Domain Recommendation](https://dl.acm.org/doi/pdf/10.1145/3477495.3531975) 1211 | - [Analyzing and Simulating User Utterance Reformulation in Conversational Recommender Systems](https://arxiv.org/pdf/2205.01763.pdf) 1212 | - [HAKG: Hierarchy-Aware Knowledge Gated Network for Recommendation](https://arxiv.org/pdf/2204.04959.pdf) 1213 | - [Self-Guided Learning to Denoise for Robust Recommendation](https://arxiv.org/pdf/2204.06832.pdf) 1214 | - [AutoGSR: Neural Architecture Search for Graph-based Session Recommendation](https://dl.acm.org/doi/pdf/10.1145/3477495.3531940) 1215 | - [Learning Graph-based Disentangled Representations for Next POI Recommendation](https://dl.acm.org/doi/pdf/10.1145/3477495.3532012) 1216 | - [GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendation](https://dl.acm.org/doi/pdf/10.1145/3477495.3531983) 1217 | - [Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential Recommendation](https://arxiv.org/pdf/2205.10775.pdf) 1218 | - [Learning to Denoise Unreliable Interactions for Graph Collaborative Filtering](https://dl.acm.org/doi/pdf/10.1145/3477495.3531889) 1219 | - [INMO: A Model-Agnostic and Scalable Module for Inductive Collaborative Filtering](https://dl.acm.org/doi/pdf/10.1145/3477495.3532000) 1220 | - [ProFairRec: Provider Fairness-aware News Recommendation](https://arxiv.org/pdf/2204.04724.pdf) 1221 | - [Multi-Faceted Global Item Relation Learning for Session-Based Recommendation](https://dl.acm.org/doi/pdf/10.1145/3477495.3532024) 1222 | - [ReCANet: A Repeat Consumption-Aware Neural Network for Next Basket Recommendation in Grocery Shopping](https://irlab.science.uva.nl/wp-content/papercite-data/pdf/ariannezhad-2022-recanet.pdf) 1223 | - [Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer](https://dl.acm.org/doi/pdf/10.1145/3477495.3532031) 1224 | - [CAPTOR: A Crowd-Aware Pre-Travel Recommender System for Out-of-Town Users](https://dl.acm.org/doi/pdf/10.1145/3477495.3531949) 1225 | - [Multi-Behavior Sequential Transformer Recommender](https://dl.acm.org/doi/pdf/10.1145/3477495.3532023) 1226 | - [Deployable and Continuable Meta-Learning-Based Recommender System with Fast User-Incremental Updates](https://dl.acm.org/doi/pdf/10.1145/3477495.3531964) 1227 | - [Explainable Fairness for Feature-aware Recommender Systems](https://arxiv.org/pdf/2204.11159.pdf) 1228 | - [Graph Trend Filtering Networks for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3477495.3531985) 1229 | - [AutoLossGen: Automatic Loss Function Generation for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3477495.3531985) 1230 | - [Determinantal Point Process Set Likelihood-Based Loss Functions for Sequential Recommendation](https://arxiv.org/pdf/2204.11562.pdf) 1231 | - [KETCH: Knowledge Graph Enhanced Thread Recommendation in Healthcare Forums](https://dl.acm.org/doi/pdf/10.1145/3477495.3532008) 1232 | - [CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems](https://arxiv.org/pdf/2204.08085.pdf) 1233 | - [PEVAE: A hierarchical VAE for personalized explainable recommendation.](https://dl.acm.org/doi/pdf/10.1145/3477495.3532039) 1234 | - [Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation](https://arxiv.org/pdf/2205.06058.pdf) 1235 | - [Less is More: Reweighting Important Spectral Graph Features for Recommendation](https://arxiv.org/pdf/2204.11346.pdf) 1236 | 1237 | 1238 | ## RecSys 2022 1239 | 1240 | - [A GPU-specialized Inference Parameter Server for Large-Scale Deep Recommendation Models](https://dl.acm.org/doi/pdf/10.1145/3523227.3546765) 1241 | - [A User-Centered Investigation of Personal Music Tours](https://dl.acm.org/doi/pdf/10.1145/3523227.3546776) 1242 | - [Adversary or Friend? An adversarial Approach to Improving Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3523227.3546784) 1243 | - [Aspect Re-distribution for Learning Better Item Embeddings in Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3523227.3546764) 1244 | - [BRUCE – Bundle Recommendation Using Contextualized item Embeddings](https://dl.acm.org/doi/pdf/10.1145/3523227.3546754) 1245 | - [Bundle MCR: Towards Conversational Bundle Recommendation](https://dl.acm.org/doi/pdf/10.1145/3523227.3546755) 1246 | - [CAEN: A Hierarchically Attentive Evolution Network for Item-Attribute-Change-Aware Recommendation in the Growing E-commerce Environment](https://dl.acm.org/doi/pdf/10.1145/3523227.3546773) 1247 | - [Context and Attribute-Aware Sequential Recommendation via Cross-Attention](https://dl.acm.org/doi/pdf/10.1145/3523227.3546777) 1248 | - [Countering Popularity Bias by Regularizing Score Differences](https://dl.acm.org/doi/pdf/10.1145/3523227.3546757) 1249 | - [Defending Substitution-based Profile Pollution Attacks on Sequential Recommenders](https://dl.acm.org/doi/pdf/10.1145/3523227.3546757) 1250 | - [Denoising Self-Attentive Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3523227.3546788) 1251 | - [Don’t recommend the obvious: estimate probability ratios](https://dl.acm.org/doi/pdf/10.1145/3523227.3546753) 1252 | - [Dual Attentional Higher Order Factorization Machines](https://dl.acm.org/doi/pdf/10.1145/3523227.3546789) 1253 | - [Dynamic Global Sensitivity for Differentially Private Contextual Bandits](https://dl.acm.org/doi/pdf/10.1145/3523227.3546781) 1254 | - [EANA: Reducing Privacy Risk on Large-scale Recommendation Models](https://web.archive.org/web/20220920233435id_/https://dl.acm.org/doi/pdf/10.1145/3523227.3546769) 1255 | - [Effective and Efficient Training for Sequential Recommendation using Recency Sampling](https://dl.acm.org/doi/pdf/10.1145/3523227.3546785) 1256 | - [Exploiting Negative Preference in Content-based Music Recommendation with Contrastive Learning](https://dl.acm.org/doi/pdf/10.1145/3523227.3546768) 1257 | - [Exploring the longitudinal effects of nudging on users’ music genre exploration behavior and listening preferences](https://dl.acm.org/doi/pdf/10.1145/3523227.3546772) 1258 | - [Fairness-aware Federated Matrix Factorization](https://dl.acm.org/doi/pdf/10.1145/3523227.3546771) 1259 | - [Fast And Accurate User Cold-Start Learning Using Monte Carlo Tree Search](https://dl.acm.org/doi/pdf/10.1145/3523227.3546786) 1260 | - [Global and Personalized Graphs for Heterogeneous Sequential Recommendation by Learning Behavior Transitions and User Intentions](https://dl.acm.org/doi/pdf/10.1145/3523227.3546761) 1261 | - [Identifying New Podcasts with High General Appeal Using a Pure Exploration Infinitely-Armed Bandit Strategy](https://dl.acm.org/doi/pdf/10.1145/3523227.3546766) 1262 | - [Learning Recommendations from User Actions in the Item-poor Insurance Domain](https://dl.acm.org/doi/pdf/10.1145/3523227.3546775) 1263 | - [Learning to Ride a Buy-Cycle: A Hyper-Convolutional Model for Next Basket Repurchase Recommendation](https://dl.acm.org/doi/pdf/10.1145/3523227.3546763) 1264 | - [MARRS: A Framework for multi-objective risk-aware route recommendation using Multitask-Transformer](https://dl.acm.org/doi/pdf/10.1145/3523227.3546787) 1265 | - [Modeling Two-Way Selection Preference for Person-Job Fit](https://dl.acm.org/doi/pdf/10.1145/3523227.3546752) 1266 | - [Modeling User Repeat Consumption Behavior for Online Novel Recommendation](https://dl.acm.org/doi/pdf/10.1145/3523227.3546762) 1267 | - [Multi-Modal Dialog State Tracking for Interactive Fashion Recommendation](https://dl.acm.org/doi/pdf/10.1145/3523227.3546774) 1268 | - [Off-Policy Actor Critic for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3523227.3546756) 1269 | - [ProtoMF: Prototype-based Matrix Factorization for Effective and Explainable Recommendations](https://dl.acm.org/doi/pdf/10.1145/3523227.3546756) 1270 | - [RADio – Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendations](https://dl.acm.org/doi/pdf/10.1145/3523227.3546780) 1271 | - [Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)](https://arxiv.org/pdf/2203.13366.pdf) 1272 | - [Reducing Cross-Topic Political Homogenization in Content-Based News Recommendation](https://dl.acm.org/doi/pdf/10.1145/3523227.3546782) 1273 | - [Self-Supervised Bot Play for Transcript-Free Conversational Recommendation with Rationales](https://dl.acm.org/doi/pdf/10.1145/3523227.3546783) 1274 | - [Solving Diversity-Aware Maximum Inner Product Search Efficiently and Effectively](https://dl.acm.org/doi/pdf/10.1145/3523227.3546779) 1275 | - [TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3523227.3546760) 1276 | - [Toward Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity](https://arxiv.org/pdf/2206.02633.pdf) 1277 | - [Towards Psychologically Grounded Dynamic Preference Models](https://dl.acm.org/doi/pdf/10.1145/3523227.3546778) 1278 | - [You Say Factorization Machine, I Say Neural Network – It’s All in the Activation](https://dl.acm.org/doi/pdf/10.1145/3523227.3551499) 1279 | - [Revisiting the Performance of iALS on Item Recommendation Benchmarks](https://dl.acm.org/doi/pdf/10.1145/3523227.3548486) 1280 | 1281 | 1282 | ## KDD 2022 1283 | 1284 | - [Comprehensive Fair Meta-learned Recommender System](https://dl.acm.org/doi/pdf/10.1145/3534678.3539269) 1285 | - [Graph-Flashback Network for Next Location Recommendation](https://dl.acm.org/doi/pdf/10.1145/3534678.3539383) 1286 | - [Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation](https://arxiv.org/pdf/2206.02115.pdf) 1287 | - [GUIDE: Group Equality Informed Individual Fairness in Graph Neural Networks](https://dl.acm.org/doi/pdf/10.1145/3534678.3539346) 1288 | - [Detecting Arbitrary Order Beneficial Feature Interactions for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3534678.3539238) 1289 | - [Practical Counterfactual Policy Learning for Top-K Recommendations](https://dl.acm.org/doi/pdf/10.1145/3534678.3539295) 1290 | - [Addressing Unmeasured Confounder for Recommendation with Sensitivity Analysis](https://dl.acm.org/doi/pdf/10.1145/3534678.3539240) 1291 | - [Towards Representation Alignment and Uniformity in Collaborative Filtering](https://dl.acm.org/doi/pdf/10.1145/3534678.3539253) 1292 | - [Knowledge-enhanced Black-box Attacks for Recommendations](https://dl.acm.org/doi/pdf/10.1145/3534678.3539359) 1293 | - [Towards Universal Sequence Representation Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3534678.3539381) 1294 | - [Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning](https://dl.acm.org/doi/pdf/10.1145/3534678.3539382) 1295 | - [Debiasing Learning for Membership Inference Attacks Against Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3534678.3539392) 1296 | - [Debiasing the Cloze Task in Sequential Recommendation with Bidirectional Transformers](https://dl.acm.org/doi/pdf/10.1145/3534678.3539430) 1297 | - [User-Event Graph Embedding Learning for Context-Aware Recommendation](https://dl.acm.org/doi/pdf/10.1145/3534678.3539458) 1298 | - [Aligning Dual Disentangled User Representations from Ratings and Textual Content](https://dl.acm.org/doi/pdf/10.1145/3534678.3539474) 1299 | - [Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking](https://dl.acm.org/doi/pdf/10.1145/3534678.3539353) 1300 | - [Make Fairness More Fair: Fair Item Utility Estimation and Exposure Re-Distribution](https://dl.acm.org/doi/pdf/10.1145/3534678.3539354) 1301 | - [Invariant Preference Learning for General Debiasing in Recommendation](https://dl.acm.org/doi/pdf/10.1145/3534678.3539439) 1302 | - [PARSRec: Explainable Personalized Attention-fused Recurrent Sequential Recommendation Using Session Partial Actions](https://arxiv.org/pdf/2209.13015.pdf) 1303 | - [CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation](https://arxiv.org/pdf/2206.00242.pdf) 1304 | - [HICF: Hyperbolic Informative Collaborative Filtering](https://dl.acm.org/doi/pdf/10.1145/3534678.3539475) 1305 | - [Extracting Relevant Information from User's Utterances in Conversational Search and Recommendation](https://dl.acm.org/doi/pdf/10.1145/3534678.3539471) 1306 | - [Counteracting User Attention Bias in Music Streaming Recommendation via Reward Modification](https://dl.acm.org/doi/pdf/10.1145/3534678.3539393) 1307 | - [Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3534678.3539461) 1308 | - [Self-Supervised Hypergraph Transformer for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3534678.3539473) 1309 | - [PinnerFormer: Sequence Modeling for User Representation at Pinterest](https://arxiv.org/pdf/2205.04507.pdf) 1310 | - [TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation](https://arxiv.org/pdf/2202.05387.pdf) 1311 | 1312 | 1313 | ## CIKM 2022 1314 | 1315 | - [A Biased Sampling Method for Imbalanced Personalized Ranking](https://dl.acm.org/doi/pdf/10.1145/3511808.3557218) 1316 | - [Accurate Action Recommendation for Smart Home via Two-Level Encoders and Commonsense Knowledge](https://dl.acm.org/doi/pdf/10.1145/3511808.3557226) 1317 | - [Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation](https://dl.acm.org/doi/pdf/10.1145/3511808.3557229) 1318 | - [An Uncertainty-Aware Imputation Framework for Alleviating the Sparsity Problem in Collaborative Filtering](https://dl.acm.org/doi/pdf/10.1145/3511808.3557236) 1319 | - [Asymmetrical Context-aware Modulation for Collaborative Filtering Recommendation](https://dl.acm.org/doi/pdf/10.1145/3511808.3557240) 1320 | - [Automatic Meta-Path Discovery for Effective Graph-Based Recommendation](https://dl.acm.org/doi/pdf/10.1145/3511808.3557244) 1321 | - [Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest Sustainabilit](https://dl.acm.org/doi/pdf/10.1145/3511808.3557415) 1322 | - [ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3511808.3557268) 1323 | - [Cross-Domain Aspect Extraction using Transformers Augmented with Knowledge Graphs](https://dl.acm.org/doi/pdf/10.1145/3511808.3557275) 1324 | - [Cross-domain Cross-architecture Black-box Attacks on Fine-tuned Models with Transferred Evolutionary Strategies](https://dl.acm.org/doi/pdf/10.1145/3511808.3557276) 1325 | - [Cross-domain Recommendation via Adversarial Adaptation](https://dl.acm.org/doi/pdf/10.1145/3511808.3557277) 1326 | - [Disentangling Past-Future Modeling in Sequential Recommendation via Dual Networks](https://scholar.google.com/scholar?hl=ko&as_sdt=0%2C5&as_ylo=2022&q=Cross-domain+Recommendation+via+Adversarial+Adaptation&btnG=) 1327 | - [Domain-Agnostic Constrastive Representations for Learning from Label Proportions](https://dl.acm.org/doi/pdf/10.1145/3511808.3557293) 1328 | - [Dual-Task Learning for Multi-Behavior Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3511808.3557298) 1329 | - [Dynamic Causal Collaborative Filtering](https://dl.acm.org/doi/pdf/10.1145/3511808.3557300) 1330 | - [Dynamic Hypergraph Learning for Collaborative Filtering](https://dl.acm.org/doi/pdf/10.1145/3511808.3557301) 1331 | - [Graph Based Long-Term And Short-Term Interest Model for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3511808.3557336) 1332 | - [Gromov-Wasserstein Guided Representation Learning for Cross-Domain Recommendation](https://dl.acm.org/doi/pdf/10.1145/3511808.3557338) 1333 | - [Hierarchical Item Inconsistency Signal learning for Sequence Denoising in Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3511808.3557348) 1334 | - [Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search](https://dl.acm.org/doi/pdf/10.1145/3511808.3557351) 1335 | - [HySAGE: A Hybrid Static and Adaptive Graph Embedding Network for Context-Drifting Recommendations](https://dl.acm.org/doi/pdf/10.1145/3511808.3557354) 1336 | - [ITSM-GCN: Informative Training Sample Mining for Graph Convolution Network-based Collaborative Filtering](https://dl.acm.org/doi/pdf/10.1145/3511808.3557368) 1337 | - [KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3511808.3557220) 1338 | - [MARIO: Modality-Aware Attention and Modality-Preserving Decoders for Multimedia Recommendation](https://dl.acm.org/doi/pdf/10.1145/3511808.3557387) 1339 | - [Memory Bank Augmented Long-tail Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3511808.3557391) 1340 | - [Multi-level Contrastive Learning Framework for Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3511808.3557404) 1341 | - [OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3511808.3557411) 1342 | - [Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3511808.3557423) 1343 | - [Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation](https://dl.acm.org/doi/pdf/10.1145/3511808.3557434) 1344 | - [SVD-GCN: A Simplified Graph Convolution Paradigm for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3511808.3557462) 1345 | - [Spatiotemporal-aware Session-based Recommendation with Graph Neural Networks](https://dl.acm.org/doi/pdf/10.1145/3511808.3557458) 1346 | - [MAE4Rec: Storage-saving Transformer for Sequential Recommendations](https://dl.acm.org/doi/pdf/10.1145/3511808.3557461) 1347 | - [Target Interest Distillation for Multi-Interest Recommendation](https://dl.acm.org/doi/pdf/10.1145/3511808.3557464) 1348 | - [The Interaction Graph Auto-encoder Network Based on Topology-aware for Transferable Recommendation](https://dl.acm.org/doi/pdf/10.1145/3511808.3557471) 1349 | - [Tiger: Transferable Interest Graph Embedding for Domain-Level Zero-Shot Recommendation](https://dl.acm.org/doi/pdf/10.1145/3511808.3557472) 1350 | - [Towards Principled User-side Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3511808.3557476) 1351 | - [Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Models](https://dl.acm.org/doi/pdf/10.1145/3511808.3557479) 1352 | - [Two-level Graph Path Reasoning for Conversational Recommendation with User Realistic Preference](https://dl.acm.org/doi/pdf/10.1145/3511808.3557482) 1353 | 1354 | 1355 | ## WSDM 2022 1356 | 1357 | - [Long Short-Term Temporal Meta-learning in Online Recommendation](https://dl.acm.org/doi/pdf/10.1145/3488560.3498371) 1358 | - [Multi-Sparse-Domain Collaborative Recommendation via Enhanced Comprehensive Aspect Preference Learning](https://dl.acm.org/doi/pdf/10.1145/3488560.3498381) 1359 | - [RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation](https://dl.acm.org/doi/pdf/10.1145/3488560.3498388) 1360 | - [Personalized Transfer of User Preferences for Cross-domain Recommendation](https://dl.acm.org/doi/pdf/10.1145/3488560.3498392) 1361 | - [Graph Collaborative Reasoning](https://dl.acm.org/doi/pdf/10.1145/3488560.3498410) 1362 | - [Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation](https://dl.acm.org/doi/pdf/10.1145/3488560.3498419) 1363 | - [Enumerating Fair Packages for Group Recommendations](https://dl.acm.org/doi/pdf/10.1145/3488560.3498432) 1364 | - [Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3488560.3498433) 1365 | - [CAN: Feature Co-Action Network for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3488560.3498435) 1366 | - [VAE++: Variational AutoEncoder for Heterogeneous One-Class Collaborative Filtering](https://dl.acm.org/doi/pdf/10.1145/3488560.3498436) 1367 | - [On Sampling Collaborative Filtering Datasets](https://arxiv.org/pdf/2201.04768.pdf) 1368 | - [Triangle Graph Interest Network for Click-through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3488560.3498458) 1369 | - [Show Me the Whole World: Towards Entire Item Space Exploration for Interactive Personalized Recommendations](https://dl.acm.org/doi/pdf/10.1145/3488560.3498459) 1370 | - [S-Walk: Accurate and Scalable Session-based Recommendation with Random Walks](https://dl.acm.org/doi/pdf/10.1145/3488560.3498464) 1371 | - [Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning](https://dl.acm.org/doi/pdf/10.1145/3488560.3498471) 1372 | - [Modeling Users’ Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search](https://dl.acm.org/doi/pdf/10.1145/3488560.3498478) 1373 | - [Toward Pareto Efficient Fairness-Utility Trade-off in Recommendation through Reinforcement Learning](https://dl.acm.org/doi/pdf/10.1145/3488560.3498487) 1374 | - [Supervised Advantage Actor-Critic for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3488560.3498494) 1375 | - [Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation](https://dl.acm.org/doi/pdf/10.1145/3488560.3498505) 1376 | - [Personalized Long-distance Fuel-efficient Route Recommendation Through Historical Trajectories Mining](https://dl.acm.org/doi/pdf/10.1145/3488560.3498512) 1377 | - [C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System](https://dl.acm.org/doi/pdf/10.1145/3488560.3498514) 1378 | - [Reinforcement Learning over Sentiment-Augmented Knowledge Graphs towards Accurate and Explainable Recommendation](https://dl.acm.org/doi/pdf/10.1145/3488560.3498515) 1379 | - [A Cooperative Neural Information Retrieval Pipeline with Knowledge Enhanced Automatic Query Reformulation](https://dl.acm.org/doi/pdf/10.1145/3488560.3498516) 1380 | - [Profiling the Design Space for Graph Neural Networks based Collaborative Filtering](https://dl.acm.org/doi/pdf/10.1145/3488560.3498520) 1381 | - [Towards Unbiased and Robust Causal Ranking for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3488560.3498521) 1382 | - [Learning Multi-granularity Consecutive User Intent Unit for Session-based Recommendation](https://dl.acm.org/doi/pdf/10.1145/3488560.3498524) 1383 | - [Contrastive Meta Learning with Behavior Multiplicity for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3488560.3498527) 1384 | 1385 | 1386 | ## WWW 2022 1387 | 1388 | - [Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework](https://dl.acm.org/doi/pdf/10.1145/3485447.3512048) 1389 | - [A Model-Agnostic Causal Learning Framework for Recommendation using Search Data](https://dl.acm.org/doi/pdf/10.1145/3485447.3511951) 1390 | - [CAUSPref: Causal Preference Learning for Out-of-Distribution Recommendation](https://dl.acm.org/doi/pdf/10.1145/3485447.3511969) 1391 | - [FairGAN: GANs-based Fairness-aware Learning for Recommendations with Implicit Feedback](https://dl.acm.org/doi/pdf/10.1145/3485447.3511958) 1392 | - [Implicit User Awareness Modeling via Candidate Items for CTR Prediction in Search Ads](https://dl.acm.org/doi/pdf/10.1145/3485447.3511953) 1393 | - [Modeling User Behavior with Graph Convolution for Personalized Product Search](https://dl.acm.org/doi/pdf/10.1145/3485447.3511949) 1394 | - [Optimizing Rankings for Recommendation in Matching Markets](https://dl.acm.org/doi/pdf/10.1145/3485447.3511961) 1395 | - [PNMTA: A Pretrained Network Modulation and Task Adaptation Approach for User Cold-Start Recommendation](https://dl.acm.org/doi/pdf/10.1145/3485447.3511963) 1396 | - [Path Language Modeling over Knowledge Graphs for Explainable Recommendation](https://dl.acm.org/doi/pdf/10.1145/3485447.3511937) 1397 | - [Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendation](https://dl.acm.org/doi/pdf/10.1145/3485447.3512166) 1398 | - [Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation](https://dl.acm.org/doi/pdf/10.1145/3485447.3512192) 1399 | - [Will You Accept the AI Recommendation? Predicting Human Behavior in AI-Assisted Decision Making](https://dl.acm.org/doi/pdf/10.1145/3485447.3512240) 1400 | - [AutoField: Automating Feature Selection in Deep Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3485447.3512071) 1401 | - [CBR: Context Bias aware Recommendation for Debiasing User Modeling and Click Prediction](https://dl.acm.org/doi/pdf/10.1145/3485447.3512099) 1402 | - [Choice of Implicit Signal Matters: Accounting for User Aspirations in Podcast Recommendations](https://dl.acm.org/doi/pdf/10.1145/3485447.3512115) 1403 | - [Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering](https://dl.acm.org/doi/pdf/10.1145/3485447.3512070) 1404 | - [Cross Pairwise Ranking for Unbiased Item Recommendation](https://dl.acm.org/doi/pdf/10.1145/3485447.3512010) 1405 | - [Deep Unified Representation for Heterogeneous Recommendation](https://dl.acm.org/doi/pdf/10.1145/3485447.3512087) 1406 | - [Disentangling Long and Short-Term Interests for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3485447.3512098) 1407 | - [Efficient Online Learning to Rank for Sequential Music Recommendation](https://dl.acm.org/doi/pdf/10.1145/3485447.3512116) 1408 | - [Fast Variational AutoEncoder with Inverted Multi-Index for Collaborative Filtering](https://dl.acm.org/doi/pdf/10.1145/3485447.3512068) 1409 | - [FeedRec: News Feed Recommendation with Various User Feedbacks](https://dl.acm.org/doi/pdf/10.1145/3485447.3512082) 1410 | - [Filter-enhanced MLP is All You Need for Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3485447.3512111) 1411 | - [FIRE: Fast Incremental Recommendation with Graph Signal Processing](https://dl.acm.org/doi/pdf/10.1145/3485447.3512108) 1412 | - [Generative Session-based Recommendation](https://dl.acm.org/doi/pdf/10.1145/3485447.3512095) 1413 | - [Graph Neural Transport Networks with Non-local Attentions for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3485447.3512162) 1414 | - [Graph-based Extractive Explainer for Recommendations](https://dl.acm.org/doi/pdf/10.1145/3485447.3512168) 1415 | - [GSL4Rec: Session-based Recommendations with Collective Graph Structure Learning and Next Interaction Prediction](https://web.archive.org/web/20220430201147id_/https://dl.acm.org/doi/pdf/10.1145/3485447.3512085) 1416 | - [Hypercomplex Graph Collaborative Filtering](https://dl.acm.org/doi/pdf/10.1145/3485447.3512065) 1417 | - [Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning](https://dl.acm.org/doi/pdf/10.1145/3485447.3512104) 1418 | - [Intent Contrastive Learning for Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3485447.3512090) 1419 | - [Learn over Past, Evolve for Future: Search-based Time-aware Recommendation with Sequential Behavior Data](https://dl.acm.org/doi/pdf/10.1145/3485447.3512117) 1420 | - [Learning Robust Recommenders through Cross-Model Agreement](https://dl.acm.org/doi/pdf/10.1145/3485447.3512202) 1421 | - [Learning to Augment for Casual User Recommendation](https://dl.acm.org/doi/pdf/10.1145/3485447.3512147) 1422 | - [MCL: Mixed-Centric Loss for Collaborative Filtering](https://dl.acm.org/doi/pdf/10.1145/3485447.3512106) 1423 | - [MINDSim: User Simulator for News Recommenders](https://dl.acm.org/doi/pdf/10.1145/3485447.3512080) 1424 | - [Modality Matches Modality: Pretraining Modality-Disentangled Item Representations for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3485447.3512079) 1425 | - [Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation](https://dl.acm.org/doi/pdf/10.1145/3485447.3512088) 1426 | - [Mutually-Regularized Dual Collaborative Variational Auto-encoder for Recommendation Systems](https://dl.acm.org/doi/pdf/10.1145/3485447.3512110) 1427 | - [Off-policy Learning over Heterogeneous Information for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3485447.3512072) 1428 | - [Rating Distribution Calibration for Selection Bias Mitigation in Recommendations](https://dl.acm.org/doi/pdf/10.1145/3485447.3512078) 1429 | - [Re4: Learning to Re-contrast, Re-attend, Re-construct for Multi-interest Recommendation](https://dl.acm.org/doi/pdf/10.1145/3485447.3512094) 1430 | - [Sequential Recommendation via Stochastic Self-Attention](https://dl.acm.org/doi/pdf/10.1145/3485447.3512077) 1431 | - [Sequential Recommendation with Decomposed Item Feature Routing](https://dl.acm.org/doi/pdf/10.1145/3485447.3512101) 1432 | - [Stochastic-Expert Variational Autoencoder for Collaborative Filtering](https://dl.acm.org/doi/pdf/10.1145/3485447.3512120) 1433 | - [Towards Automatic Discovering of Deep Hybrid Network Architecture for Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3485447.3512066) 1434 | - [Unbiased Sequential Recommendation with Latent Confounders](https://dl.acm.org/doi/pdf/10.1145/3485447.3512092) 1435 | - [A Contrastive Sharing Model for Multi-Task Recommendation](https://dl.acm.org/doi/pdf/10.1145/3485447.3512043) 1436 | - [Accurate and Explainable Recommendation via Review Rationalization](https://dl.acm.org/doi/pdf/10.1145/3485447.3512029) 1437 | - [Comparative Explanations of Recommendations](https://dl.acm.org/doi/pdf/10.1145/3485447.3512031) 1438 | - [Recommendation Unlearning](https://dl.acm.org/doi/pdf/10.1145/3485447.3511997) 1439 | - [STAM: A Spatiotemporal Aggregation Method for Graph Neural Network-based Recommendation](https://dl.acm.org/doi/pdf/10.1145/3485447.3512041) 1440 | - [VisGNN: Personalized Visualization Recommendation via Graph Neural Networks](https://dl.acm.org/doi/pdf/10.1145/3485447.3512001) 1441 | - [Who to Watch Next: Two-side Interactive Networks for Live Broadcast Recommendation](https://dl.acm.org/doi/pdf/10.1145/3485447.3511939) 1442 | - [Causal Representation Learning for Out-of-Distribution Recommendation](https://dl.acm.org/doi/pdf/10.1145/3485447.3512251) 1443 | - [End-to-end Learning for Fair Ranking Systems](https://dl.acm.org/doi/pdf/10.1145/3485447.3512247) 1444 | - [Following Good Examples – Health Goal-Oriented Food Recommendation based on Behavior Data](https://dl.acm.org/doi/pdf/10.1145/3485447.3514193) 1445 | - [Link Recommendations for PageRank Fairness](https://dl.acm.org/doi/pdf/10.1145/3485447.3512249) 1446 | - [DCAF-BERT: A Distilled Cachable Adaptable Factorized Model For Improved Ads CTR Prediction](https://assets.amazon.science/db/32/647b9dba4f7e8740780d63b90aa5/dcaf-bert-a-distilled-cachable-adaptable-factorized-model-for-improved-ads-ctr-prediction.pdf) 1447 | - [DC-GNN: Decoupled Graph Neural Networks for Improving and Accelerating Large-Scale E-commerce Retrieval](https://dl.acm.org/doi/pdf/10.1145/3487553.3524203) 1448 | - [Simgrace: A Simple Framework for graph contrastive learning without data augmentation](https://dl.acm.org/doi/pdf/10.1145/3485447.3512156) 1449 | 1450 | 1451 | ## IJCAI 2022 1452 | 1453 | - [Towards Resolving Propensity Contradiction in Offline Recommender Learning](https://usaito.github.io/files/IJCAI2022_DAMF.pdf) 1454 | - [MLP4Rec: A Pure MLP Architecture for Sequential Recommendations](https://arxiv.org/pdf/2204.11510.pdf) 1455 | - [Discrete Listwise Personalized Ranking for Fast Top-N Recommendation with Implicit Feedback](https://www.ijcai.org/proceedings/2022/0300.pdf) 1456 | - [HCFRec: Hash Collaborative Filtering via Normalized Flow with Structural Consensus for Efficient Recommendation](https://arxiv.org/pdf/2205.12042.pdf) 1457 | - [Enhancing Sequential Recommendation with Graph Contrastive Learning](https://arxiv.org/pdf/2205.14837.pdf) 1458 | - [RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation](https://arxiv.org/pdf/2205.14005.pdf) 1459 | - [Modeling Spatio-temporal Neighbourhood for Personalized Point-of-interest Recommendation](https://www.ijcai.org/proceedings/2022/0490.pdf) 1460 | - [Heterogeneous Interactive Snapshot Network for Review-Enhanced Stock Profiling and Recommendation](https://www.ijcai.org/proceedings/2022/0550.pdf) 1461 | - [Next Point-of-Interest Recommendation with Inferring Multi-step Future Preferences](https://www.ijcai.org/proceedings/2022/0521.pdf) 1462 | - [Poisoning Deep Learning Based Recommender Model in Federated Learning Scenarios](https://arxiv.org/pdf/2204.13594.pdf) 1463 | - [Self-supervised Graph Neural Networks for Multi-behavior Recommendation](http://www.shichuan.org/doc/134.pdf) 1464 | - [Trading Hard Negatives and True Negatives: A Debiased Contrastive Collaborative Filtering Approach](https://arxiv.org/pdf/2204.11752.pdf) 1465 | 1466 | 1467 | ## ICML 2022 1468 | 1469 | - [Estimating and Penalizing Induced Preference Shifts in Recommender Systems](https://proceedings.mlr.press/v162/carroll22a/carroll22a.pdf) 1470 | - [Learning from a Learning User for Optimal Recommendations](https://proceedings.mlr.press/v162/yao22a/yao22a.pdf) 1471 | 1472 | 1473 | 1474 | 1475 | 1476 | 1477 | 1478 | 1479 | ## Recommendation 1480 | 1481 | ### Models 1482 | 1483 | ### 2023 1484 | 1485 | * DCAH: [Search Behavior Prediction: A Hypergraph Perspective](https://arxiv.org/pdf/2211.13328.pdf) (WSDM'23) 1486 | * CLUE: [Scaling Law for Recommendation Models: Towards General-purpose User Representations](https://arxiv.org/pdf/2111.11294.pdf) (AAAI'23) 1487 | 1488 | ### 2022 1489 | 1490 | * PinnerFormer: [PinnerFormer: Sequence Modeling for User Representation at Pinterest](https://arxiv.org/pdf/2205.04507.pdf) (KDD'22) 1491 | * ItemSage: [ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest](https://arxiv.org/pdf/2205.11728.pdf) (KDD'22) 1492 | * DuoRec: [Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation](https://arxiv.org/pdf/2110.05730.pdf) (WSDM'22) 1493 | * FMLP-Rec: [FilterEnhanced MLP is All You Need for Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3485447.3512111) (WWW'22) 1494 | * CML: [Contrastive Meta Learning with Behavior Multiplicity for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3488560.3498527) (WSDM'22) 1495 | * Tiger: [Tiger: Transferable Interest Graph Embedding for Domain-Level Zero-Shot Recommendation](https://dl.acm.org/doi/pdf/10.1145/3511808.3557472) (CIKM'22) 1496 | * AiRS: [AiRS: A Large-Scale Recommender System at NAVER News](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9835494) (ICDE'22) 1497 | * CoRGi: [CORGI: Content-Rich Graph Neural Networks with Attention](https://dl.acm.org/doi/pdf/10.1145/3534678.3539306) (KDD'22) 1498 | * SimGCL: [Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3477495.3531937) (SIGIR'22) 1499 | * HCCF: [Hypergraph Contrastive Collaborative Filtering](https://dl.acm.org/doi/pdf/10.1145/3477495.3532058) (SIGIR'22) 1500 | * NCL: [Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning](https://dl.acm.org/doi/pdf/10.1145/3485447.3512104) (WWW'22) 1501 | * SHT: [Self-Supervised Hypergraph Transformer for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3534678.3539473) (KDD'22) 1502 | * SimGRACE: [Simgrace: A Simple Framework for graph contrastive learning without data augmentation](https://dl.acm.org/doi/pdf/10.1145/3485447.3512156) (WWW'22) 1503 | * AutoGCL: [Autogcl: Automated graph contrastive learning via learnable view generators](https://arxiv.org/pdf/2109.10259.pdf) (AAAI'22) 1504 | * SAIL: [SAIL: Self-Augmented Graph Contrastive Learning](https://arxiv.org/pdf/2009.00934.pdf) (AAAI'22) 1505 | * UniSRec: [Towards Universal Sequence Representation Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3534678.3539381) (KDD'22) 1506 | 1507 | 1508 | ### 2021 1509 | * MAIL: [Zero Shot on the Cold-Start Problem: Model-Agnostic Interest Learning for Recommender Systems](https://dl.acm.org/doi/abs/10.1145/3459637.3482312) (CIKM'21) 1510 | * Transformer4Rec: [Transformers4Rec: Bridging the Gap between NLP and Sequential / Session-Based Recommendation](https://dl.acm.org/doi/pdf/10.1145/3460231.3474255) (Recsys'21) 1511 | * NGF: [Neural graph filtering for context-aware recommendation](https://proceedings.mlr.press/v157/chuanyan21a/chuanyan21a.pdf) (ACML'21) 1512 | * SGL: [Self-supervised Graph Learning for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3404835.3462862) (SIGIR'21) 1513 | * MHCN: [Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation](https://dl.acm.org/doi/pdf/10.1145/3442381.3449844) (WWW'21) 1514 | * DHCN: [Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation](https://www.aaai.org/AAAI21Papers/AAAI-1889.XiaX.pdf?ref=https://githubhelp.com) (AAAI'21) 1515 | * MINCE: [Memory Augmented MultiInstance Contrastive Predictive Coding for Sequential Recommendation](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9678990) (ICDM'21) 1516 | * SEPT: [Socially-Aware Self-Supervised Tri-Training for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3447548.3467340) (KDD'21) 1517 | * BUIR: [Bootstrapping User and Item Representations for One-Class Collaborative Filtering](https://dl.acm.org/doi/pdf/10.1145/3404835.3462935) (SIGIR'21) 1518 | * UltraGCN: [Ultra Simplification of Graph Convolutional Networks for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3459637.3482291) (CIKM'21) 1519 | * MixGCF: [MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3447548.3467408) (KDD'21) 1520 | * SERec: [An Efficient and Effective Framework for Session-based Social Recommendation](https://dl.acm.org/doi/pdf/10.1145/3437963.3441792) (WSDM'21) 1521 | * CL4SRec: [Contrastive Learning for Sequential Recommendation](https://arxiv.org/pdf/2010.14395.pdf) (SIGIR'21) 1522 | * GCA: [Graph Contrastive Learning with Adaptive Augmentation](https://dl.acm.org/doi/pdf/10.1145/3442381.3449802) (WWW'21) 1523 | 1524 | 1525 | ### 2020 1526 | * PinnerSage: [PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest](https://dl.acm.org/doi/pdf/10.1145/3394486.3403280) (KDD'20) 1527 | * TAFA: [TAFA: Two-headed attention fused autoencoder for context-aware recommendations](https://dl.acm.org/doi/pdf/10.1145/3383313.3412268) (Recsys'20) 1528 | * MBCN: [Multi-Branch Convolutional Network for Context-Aware Recommendation](https://dl.acm.org/doi/pdf/10.1145/3397271.3401218) (SIGIR'20) 1529 | * ENSFM: [Efficient Non-Sampling Factorization Machines for Optimal Context-Aware Recommendation](https://dl.acm.org/doi/pdf/10.1145/3366423.3380303) (WWW'20) 1530 | * S3-Rec: [S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization](https://dl.acm.org/doi/pdf/10.1145/3340531.3411954) (CIKM'20) 1531 | * DMR: [Deep Match to Rank Model for Personalized Click-Through Rate Prediction](https://www.atailab.cn/ir2020fall/pdf/gaoyulan.pdf) (AAAI'20) 1532 | * EHCF: [Efficient heterogeneous collaborative filtering without neg-ative sampling for recommendation](http://www.thuir.cn/group/~YQLiu/publications/AAAI2020Chen.pdf) (AAAI'20) 1533 | * SCE-GNN: [Global Context Enhanced Graph Neural Networks for Session-based Recommendation](https://dl.acm.org/doi/pdf/10.1145/3397271.3401142) (SIGIR'20) 1534 | * SSG: [Set-Sequence-Graph: A Multi-View Approach Towards Exploiting Reviews for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3340531.3411939) (CIKM'20) 1535 | * SML: [Symmetric Metric Learning with Adaptive Margin for Recommendation](https://www.researchgate.net/profile/Shuai-Zhang-12/publication/342542890_Symmetric_Metric_Learning_with_Adaptive_Margin_for_Recommendation/links/6002ea7645851553a049d2e8/Symmetric-Metric-Learning-with-Adaptive-Margin-for-Recommendation.pdf) (AAAI'20) 1536 | * KHGT: [Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation](https://www.aaai.org/AAAI21Papers/AAAI-3071.XiaL.pdf) (AAAI'20) 1537 | * LCF: [Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters](http://proceedings.mlr.press/v119/yu20e/yu20e.pdf) (PMLR'20) 1538 | * SEE-PT: [SEE-PT: Sequential recommendation via personalized transformer](https://dl.acm.org/doi/abs/10.1145/3383313.3412258) (RecSys'20) 1539 | * LightGCN: [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://arxiv.org/pdf/2002.02126.pdf) (SIGIR'20) 1540 | * MBGCN: [Multi-behavior Recommendation with Graph Convolutional Networks](https://dl.acm.org/doi/pdf/10.1145/3397271.3401072) (SIGIR'20) 1541 | * MA-GNN: [Memory Augmented Graph Neural Networks for Sequential Recommendation](https://www.atailab.cn/seminar2020spring/pdf/Memory%20Augmented%20Graph%20Neural%20Networks%20for%20Sequential%20Recommendation.pdf) (AAAI'20) 1542 | * GCCF: [Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach](https://arxiv.org/pdf/2001.10167.pdf) (AAAI'20) 1543 | * HyRec: [Next-item Recommendation with Sequential Hypergraphs](https://dl.acm.org/doi/pdf/10.1145/3397271.3401133) (SIGIR'20) 1544 | * DGCF: [Disentangled Graph Collaborative Filtering](https://dl.acm.org/doi/pdf/10.1145/3397271.3401137) (SIGIR'20) 1545 | * GRACE: [Deep Graph Contrastive Representation Learning](https://arxiv.org/pdf/2006.04131.pdf) (arXiv'20) 1546 | 1547 | 1548 | ### 2019 1549 | 1550 | * LLAE: [From Zero-Shot Learning to Cold-Start Recommendation](https://ojs.aaai.org/index.php/AAAI/article/view/4324) (AAAI'19) 1551 | * MetaEmb: [Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings](https://dl.acm.org/doi/pdf/10.1145/3331184.3331268) (SIGIR'19) 1552 | * NMTR: [Neural Multi-Task Recommendation from Multi-Behavior Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8731537) (ICDE'19) 1553 | * NGCF: [Neural Graph Collaborative Filtering](https://dl.acm.org/doi/pdf/10.1145/3331184.3331267) (SIGIR'19) 1554 | * BERT4Rec: [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://arxiv.org/pdf/1904.06690.pdf) (CIKM'19) 1555 | * KGAT: [KGAT: Knowledge Graph Attention Network for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3292500.3330989) (KDD'19) 1556 | * KGCN: [Knowledge Graph Convolutional Networks for Recommender Systems](https://arxiv.org/pdf/1904.12575.pdf) (WWW'19) 1557 | * GraphRec: [Graph Neural Networks for Social Recommendation](https://arxiv.org/pdf/1902.07243.pdf) (WWW'19) 1558 | * NARRE: [Neural Attentional Rating Regression with Review-level Explanations](https://dl.acm.org/doi/pdf/10.1145/3178876.3186070) (WWW'19) 1559 | * METAS: [Action Space Learning for Heterogeneous User Behavior Prediction](https://dsail.kaist.ac.kr/files/IJCAI19_2.pdf) (IJCAI'19) 1560 | * SR-GNN: [Session-based recommendation with graph neural networks](https://www.atailab.cn/ir2019fall/pdf/chenyunqin.pdf) (AAAI'19) 1561 | * SelCa: [Recommender System Using Sequential and Global Preference via Attention Mechanism and Topic Modeling 1562 | ](https://dl.acm.org/doi/pdf/10.1145/3357384.3358054?casa_token=kO28r-0VvmcAAAAA:fKPujREdM3yNGRUphi9zPvzDbmnzAwy715zunIkTPyPszz9As-H_TAzdDitI0PBJDutyNVFcS_2Fsw8L) (CIKM'19) 1563 | * MMGCN: [MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video](https://weiyinwei.github.io/papers/mmgcn.pdf) (MM'19) 1564 | 1565 | 1566 | ### 2018 1567 | * Caser: [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](https://arxiv.org/pdf/1809.07426.pdf) (WSDM'18) 1568 | * PinSage: [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3219819.32198900) (KDD'18) 1569 | * HIN: [Adaptive Implicit Friends Identification over Heterogeneous Network for Social Recommendation](https://dl.acm.org/doi/pdf/10.1145/3269206.3271725) (CIKM'18) 1570 | * VAE: [Variational Autoencoders for Collaborative Filtering](https://dl.acm.org/doi/pdf/10.1145/3178876.3186150) (WWW'18) 1571 | * triple2vec: [Representing and Recommending Shopping Baskets with Complementarity, Compatibility and Loyalty](https://dl.acm.org/doi/pdf/10.1145/3269206.3271786) (CIKM'18) 1572 | * GC-MC: [Graph Convolutional Matrix Completion](https://arxiv.org/pdf/1706.02263.pdf) (KDD'18) 1573 | * SASRec: [Self-Attentive Sequential Recommendation](https://arxiv.org/pdf/1808.09781.pdf) (ICDM'18) 1574 | * SDNets: [Adversarial Distillation for Efficient Recommendation with External Knowledge](https://dl.acm.org/doi/pdf/10.1145/3281659) (TOIS'18) 1575 | * AIN: [An Attentive Interaction Network for Context-aware Recommendation](https://dl.acm.org/doi/pdf/10.1145/3269206.3271813) (CIKM'18) 1576 | * ConvNCF: [Outer Product-based Neural Collaborative Filtering](https://arxiv.org/pdf/1808.03912.pdf) (IJCAI'18) 1577 | * STAMP: [STAMP: shortterm attention/memory priority model for session-based recommendation](https://dl.acm.org/doi/pdf/10.1145/3219819.3219950) (KDD'18) 1578 | * A3CF: [An Adaptive Aspect Attention Model for Rating Prediction](https://www.ijcai.org/proceedings/2018/0521.pdf) (IJCAI'18) 1579 | 1580 | 1581 | ### 2017 1582 | * TransNet: [TransNets: Learning to Transform for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3109859.3109878) (Recsys'17) 1583 | * DeepCoNN: [Joint Deep Modeling of Users and Items Using Reviews for Recommendation](https://dl.acm.org/doi/pdf/10.1145/3018661.3018665) (WSDM'17) 1584 | * ACF: [Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention](https://dl.acm.org/doi/pdf/10.1145/3077136.3080797) (SIGIR'17) 1585 | * CML: [Collaborative Metric Learning](https://dl.acm.org/doi/pdf/10.1145/3038912.3052639) (WWW'17) 1586 | * NMF: [Neural Factorization Machines for Sparse Predictive Analytics](https://dl.acm.org/doi/pdf/10.1145/3077136.3080777) (SIGIR'17) 1587 | * DMF: [Deep matrix factorization models for recommender systems](https://web.archive.org/web/20180721070244id_/https://www.ijcai.org/proceedings/2017/0447.pdf) (IJCAI'17) 1588 | * NARM: [Neural attentive session-based recommendation](https://dl.acm.org/doi/pdf/10.1145/3132847.3132926) (CIKM'17) 1589 | * NCF: [Neural Collaborative Filtering](http://184pc128.csie.ntnu.edu.tw/presentation/19-10-18/Neural%20Collaborative%20Filtering.pdf) (WWW'17) 1590 | * GRU: [Sequential User-based Recurrent Neural Network Recommendations](https://dl.acm.org/doi/pdf/10.1145/3109859.3109877) (RecSys'17) 1591 | 1592 | 1593 | ### ~2016 1594 | * CDAE: [Collaborative Denoising Auto-Encoders for Top-N Recommender Systems](https://web.archive.org/web/20160803143925id_/http://alicezheng.org:80/papers/wsdm16-cdae.pdf) (WSDM'16) 1595 | * DREAM: [A Dynamic Recurrent Model for Next Basket Recommendation](https://dl.acm.org/doi/pdf/10.1145/2911451.2914683) (SIGIR'16) 1596 | * ConvMF: [Convolutional Matrix Factorization for Document Context-Aware Recommendation](https://web.archive.org/web/20181222123319id_/http://uclab.khu.ac.kr:80/resources/publication/C_351.pdf) (RecSys'16) 1597 | * eALS: [Fast Matrix Factorization for Online Recommendation with Implicit Feedback](http://staff.ustc.edu.cn/~hexn/papers/sigir16-eals-cm.pdf) (SIGIR'16) 1598 | * GRU4Rec: [Session-based Recommendations with Recurrent Neural Networks](https://arxiv.org/pdf/1511.06939.pdf) (ICLR'16) 1599 | * AutoRec: [AutoRec: Autoencoders Meet Collaborative Filtering](https://web.archive.org/web/20160312152337id_/http://users.cecs.anu.edu.au/~ssanner/Papers/www15.pdf) (WWW'15) 1600 | * CDL: [Collaborative Deep Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/2783258.2783273) (KDD'15) 1601 | * CSLIM: [Deviation-Based Contextual SLIM Recommenders](https://dl.acm.org/doi/pdf/10.1145/2661829.2661987) (CIKM'14) 1602 | * LogisticMF: [Logistic Matrix Factorization for Implicit Feedback](http://web.stanford.edu/~rezab/nips2014workshop/submits/logmat.pdf) (NeurIPS'14) 1603 | * HFT: [Hidden factors and hidden topics: understanding rating dimensions with review text](http://i.stanford.edu/~julian/pdfs/recsys13.pdf) (Recsys'13) 1604 | * CTR: [Collaborative topic modeling for recommending scientific articles](http://www.cs.columbia.edu/~blei/papers/WangBlei2011.pdf) (KDD'11) 1605 | * SLIM: [SLIM: Sparse Linear Methods for Top-N Recommender Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6137254) (ICDM'11) 1606 | * MF: [Matrix factorization techniques for recommender systems](https://rakaposhi.eas.asu.edu/cse494/lsi-for-collab-filtering.pdf) (MC'09) 1607 | * BPR: [BPR: Bayesian Personalized Ranking from Implicit Feedback](https://arxiv.org/ftp/arxiv/papers/1205/1205.2618.pdf) (UAI'09) 1608 | * SoRec: [SoRec: Social Recommendation Using Probabilistic Matrix Factorization](https://dl.acm.org/doi/pdf/10.1145/1458082.1458205) (CIKM'08) 1609 | * ALS: [Collaborative Filtering for Implicit Feedback Datasets](https://web.archive.org/web/20110401191554id_/http://www2.research.att.com/~yifanhu/PUB/cf.pdf) (ICDM'08) 1610 | * RBM: [Restricted Boltzmann Machines for Collaborative Filtering](https://icml.cc/imls/conferences/2007/proceedings/papers/407.pdf) (ICML'07) 1611 | * Item-Base CF: [Item-based top-N recommendation algorithms](https://emunix.emich.edu/~wsverdlik/COSC562/ItemBasedTopTen.pdf) (TOIS'04) 1612 | 1613 | 1614 | ### Others 1615 | 1616 | * [Self-supervised Learning for Large-scale Item Recommendations](https://dl.acm.org/doi/pdf/10.1145/3459637.3481952) (CIKM'21) 1617 | * [Disentangled Self-Supervision in Sequential Recommenders](https://dl.acm.org/doi/pdf/10.1145/3394486.3403091) (KDD'20) 1618 | * [The YouTube video recommendation system](https://dl.acm.org/doi/pdf/10.1145/1864708.1864770) (Recsys'16) 1619 | * [Multiverse Recommendation: N-dimensional Tensor Factorization for Context-aware Collaborative Filtering](https://dl.acm.org/doi/pdf/10.1145/1864708.1864727) (Recsys'10) 1620 | * [The Netflix prize](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.115.6998&rep=rep1&type=pdf) (KDD'07) 1621 | * [Amazon.com recommendations: item-to-item collaborative filtering](http://www.cs.umd.edu/~samir/498/Amazon-Recommendations.pdf) (MIC'03) 1622 | * [Item-based collaborative filtering recommendation algorithms](https://dl.acm.org/doi/pdf/10.1145/371920.372071?casa_token=ITskTAW3II0AAAAA:jy81AUSR7Dvr__wPVc8fqsU4djofU8HmReqC4MtYhvW75f25DpEcNmFFSJR7OxRa5LzwZdsP2GDtig) (WWW'01) 1623 | * [Learning Collaborative Information Filters](https://www.aaai.org/Papers/Workshops/1998/WS-98-08/WS98-08-005.pdf) (AAAI'98) 1624 | * [GroupLens: An Open Architecture for Collaborative Filtering of Netnews](http://www.wdyd.com.cn/blog/wp-content/uploads/2017/02/2.resnick.pdf) (CSCW'94) 1625 | 1626 | 1627 | ### Survey 1628 | 1629 | * [A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions](https://www.researchgate.net/profile/Yu-Zheng-49/publication/354889377_Graph_Neural_Networks_for_Recommender_Systems_Challenges_Methods_and_Directions/links/6350383596e83c26eb37bdfd/Graph-Neural-Networks-for-Recommender-Systems-Challenges-Methods-and-Directions.pdf) (2022) 1630 | * [Deep Learning Based Recommender System: A Survey and New Perspectives](https://dl.acm.org/doi/pdf/10.1145/3285029) (2019) 1631 | * [Recommender System Application Developments: A Survey](https://www.uts.edu.au/sites/default/files/desi-publication-recommender%20system%20application%20developments%20a%20survey-accepted%20menuscript.pdf) (2015) 1632 | --------------------------------------------------------------------------------