└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Awesome Contrastive Learning & Data Augmentation RS Paper & Code 2 | 3 | This repository collects the latest research progress of **Contrastive Learning (CL) and Data Augmentation (DA)** in Recommender Systems. 4 | Comments and contributions are welcome. 5 | 6 | CF = Collaborative Filtering, SSL = Self-Supervised Learning 7 | 8 | - [Survey/Tutorial/Framework](#Survey-Tutorial-Framework) Total Papers: 8 9 | - [Only Data Augmentation](#Only-Data-Augmentation) Total Papers: 71 10 | - [Graph Models with CL](#Graph-Models-with-CL) Total Papers: 188 11 | - [Sequential Models with CL](#Sequential-Models-with-CL) Total Papers: 151 12 | - [Other Tasks with CL](#Other-Tasks-with-CL) Total Papers: 207 13 | 14 | 15 | ## Survey-Tutorial-Framework 16 | 17 | 1. **Contrastive Self-supervised Learning in Recommender Systems: A Survey** (Survey) 18 | 19 | TOIS 2023, [[PDF]](https://arxiv.org/pdf/2303.09902.pdf) 20 | 21 | 2. **Self-Supervised Learning for Recommender Systems A Survey** (Survey + Framework) 22 | 23 | TKDE 2023, [[PDF]](https://ieeexplore.ieee.org/document/10144391), [[Code]](https://github.com/Coder-Yu/SELFRec) 24 | 25 | 3. **Self-Supervised Learning in Recommendation: Fundamentals and Advances** (Tutorial) 26 | 27 | WWW 2022, [[Web]](https://ssl-recsys.github.io/) 28 | 29 | 4. **Tutorial: Self-Supervised Learning for Recommendation: Foundations, Methods and Prospects** (Tutorial) 30 | 31 | DASFAA 2023, [[Web]](https://junliang-yu.github.io/publications/) 32 | 33 | 5. **SSLRec: A Self-Supervised Learning Framework for Recommendation** (Framework) 34 | 35 | WSDM 2024, [[PDF]](https://arxiv.org/pdf/2308.05697.pdf), [[Code]](https://github.com/HKUDS/SSLRec) 36 | 37 | 6. **A Comprehensive Survey on Self-Supervised Learning for Recommendation** (Survey) 38 | 39 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2404.03354.pdf), [[Code]](https://github.com/HKUDS/Awesome-SSLRec-Papers) 40 | 41 | 7. **Towards Graph Contrastive Learning: A Survey and Beyond** (Survey) 42 | 43 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2405.11868) 44 | 45 | 8. **Data Augmentation for Sequential Recommendation: A Survey** (Survey) 46 | 47 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2409.13545) 48 | 49 | 50 | ## Only Data Augmentation 51 | 52 | 1. **Enhancing Collaborative Filtering with Generative Augmentation** (CF + GAN + DA) 53 | 54 | KDD 2019, [[PDF]](https://dl.acm.org/doi/abs/10.1145/3292500.3330873) 55 | 56 | 2. **Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation** (Session + DA) 57 | 58 | WWW 2020, [[PDF]](https://arxiv.org/pdf/1906.04473.pdf), [[Code]](https://github.com/fajieyuan/WWW2020-grec) 59 | 60 | 3. **Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer** (Sequential + DA) 61 | 62 | SIGIR 2021, [[PDF]](https://arxiv.org/pdf/2105.00522.pdf), [[Code]](https://github.com/DyGRec/ASReP) 63 | 64 | 4. **Improving Sequential Recommendations via Bidirectional Temporal Data Augmentation with Pre-training** (Sequential + DA) 65 | 66 | TKDE 2025, [[PDF]](https://arxiv.org/pdf/2112.06460.pdf), [[Code]](https://github.com/juyongjiang/BARec) 67 | 68 | 5. **Counterfactual Data-Augmented Sequential Recommendation** (Sequential + Counterfactual + DA) 69 | 70 | SIGIR 2021, [[PDF]](https://arxiv.org/pdf/2207.02643.pdf) 71 | 72 | 6. **CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation** (Sequential + Counterfactual + DA) 73 | 74 | SIGIR 2021, [[PDF]](https://arxiv.org/pdf/2109.05261.pdf) 75 | 76 | 7. **Effective and Efficient Training for Sequential Recommendation using Recency Sampling** (Sequential + DA) 77 | 78 | RecSys 2022, [[PDF]](https://arxiv.org/pdf/2207.02643.pdf) 79 | 80 | 8. **Data Augmentation Strategies for Improving Sequential Recommender Systems** (Sequential + DA) 81 | 82 | arXiv 2022, [[PDF]](https://arxiv.org/pdf/2203.14037.pdf), [[Code]](https://github.com/saladsong/DataAugForSeqRec) 83 | 84 | 9. **Learning to Augment for Casual User Recommendation** (Sequential + DA) 85 | 86 | WWW 2022, [[PDF]](https://arxiv.org/pdf/2204.00926.pdf) 87 | 88 | 10. **Recency Dropout for Recurrent Recommender Systems** (RNN + DA) 89 | 90 | arXiv 2022, [[PDF]](https://arxiv.org/pdf/2201.11016.pdf) 91 | 92 | 11. **Improved Recurrent Neural Networks for Session-based Recommendations** (RNN + DA) 93 | 94 | DLRS 2016, [[PDF]](https://arxiv.org/pdf/1606.08117.pdf) 95 | 96 | 12. **Bootstrapping User and Item Representations for One-Class Collaborative Filtering** (CF + Graph + DA) 97 | 98 | SIGIR 2021, [[PDF]](https://arxiv.org/pdf/2105.06323.pdf), [[Code]](https://github.com/donalee/BUIR) 99 | 100 | 13. **MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems** (Graph + DA) 101 | 102 | KDD 2021, [[PDF]](http://keg.cs.tsinghua.edu.cn/jietang/publications/KDD21-Huang-et-al-MixGCF.pdf), [[Code]](https://github.com/huangtinglin/MixGCF) 103 | 104 | 14. **Improving Recommendation Fairness via Data Augmentation** (Fairness + DA) 105 | 106 | WWW 2023, [[PDF]](https://arxiv.org/pdf/2302.06333.pdf), [[Code]](https://github.com/newlei/FDA) 107 | 108 | 15. **Fairly Adaptive Negative Sampling for Recommendations** (Fairness + DA) 109 | 110 | WWW 2023, [[PDF]](https://arxiv.org/pdf/2302.08266.pdf) 111 | 112 | 16. **Creating Synthetic Datasets for Collaborative Filtering Recommender Systems using Generative Adversarial Networks** (CF + DA) 113 | 114 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2303.01297.pdf) 115 | 116 | 17. **Graph Collaborative Signals Denoising and Augmentation for Recommendation** (CF + DA) 117 | 118 | SIGIR 2023, [[PDF]](https://arxiv.org/pdf/2304.03344.pdf), [[Code]](https://github.com/zfan20/GraphDA) 119 | 120 | 18. **Data Augmented Sequential Recommendation based on Counterfactual Thinking** (Sequential + DA) 121 | 122 | TKDE 2022, [[PDF]](https://ieeexplore.ieee.org/abstract/document/9950302) 123 | 124 | 19. **Multi-Epoch Learning for Deep Click-Through Rate Prediction Models** (CRT + DA) 125 | 126 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2305.19531.pdf) 127 | 128 | 20. **Improving Conversational Recommendation Systems via Counterfactual Data Simulation** (Conversational Rec + DA) 129 | 130 | KDD 2023, [[PDF]](https://arxiv.org/pdf/2306.02842.pdf), [[Code]](https://github.com/RUCAIBox/CFCRS) 131 | 132 | 21. **Disentangled Variational Auto-encoder Enhanced by Counterfactual Data for Debiasing Recommendation** (Debias Rec + DA) 133 | 134 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2306.15961.pdf) 135 | 136 | 22. **Domain Disentanglement with Interpolative Data Augmentation for Dual-Target Cross-Domain Recommendation** (Cross-Domain + DA) 137 | 138 | RecSys 2023, [[PDF]](https://arxiv.org/pdf/2307.13910.pdf) 139 | 140 | 23. **Scaling Session-Based Transformer Recommendations using Optimized Negative Sampling and Loss Functions** (Session + DA) 141 | 142 | RecSys 2023, [[PDF]](https://arxiv.org/pdf/2307.14906.pdf) 143 | 144 | 24. **Intrinsically Motivated Reinforcement Learning based Recommendation with Counterfactual Data Augmentation** (RL Rec + DA) 145 | 146 | arXiv 2022, [[PDF]](https://arxiv.org/pdf/2209.08228.pdf) 147 | 148 | 25. **Augmented Negative Sampling for Collaborative Filtering** (CF + DA) 149 | 150 | RecSys 2023, [[PDF]](https://arxiv.org/pdf/2308.05972.pdf), [[Code]](https://github.com/Asa9aoTK/ANS-Recbole) 151 | 152 | 26. **gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative Sampling** (Sequential + DA) 153 | 154 | RecSys 2023, [[PDF]](https://arxiv.org/pdf/2308.07192.pdf) 155 | 156 | 27. **Learning from All Sides: Diversified Positive Augmentation via Self-distillation in Recommendation** (DA) 157 | 158 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2308.07629.pdf) 159 | 160 | 28. **Counterfactual Graph Augmentation for Consumer Unfairness Mitigation in Recommender Systems** (Graph + DA) 161 | 162 | CIKM 2023, [[PDF]](https://arxiv.org/abs/2308.12083), [[Code]](https://github.com/jackmedda/RS-BGExplainer/tree/cikm2023) 163 | 164 | 29. **Bayes-enhanced Multi-view Attention Networks for Robust POI Recommendation** (Graph + DA) 165 | 166 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2311.00491.pdf) 167 | 168 | 30. **Diffusion Augmentation for Sequential Recommendation** (Sequential + DA) 169 | 170 | CIKM 2023, [[PDF]](https://arxiv.org/pdf/2309.12858.pdf), [[Code]](https://github.com/liuqidong07/DiffuASR) 171 | 172 | 31. **Large Language Models as Data Augmenters for Cold-Start Item Recommendation** (DA) 173 | 174 | WWW 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3589335.3651532) 175 | 176 | 32. **SSDRec: Self-Augmented Sequence Denoising for Sequential Recommendation** (Sequential + DA) 177 | 178 | ICDE 2024, [[PDF]](https://arxiv.org/pdf/2403.04278.pdf), [[Code]](https://github.com/zc-97/SSDRec) 179 | 180 | 33. **CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation** (DA) 181 | 182 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2403.06447.pdf) 183 | 184 | 34. **ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation** (DA) 185 | 186 | WWW 2024, [[PDF]](https://arxiv.org/pdf/2308.11131.pdf), [[Code]](https://github.com/LaVieEnRose365/ReLLa) 187 | 188 | 35. **Repeated Padding for Sequential Recommendation** (Sequential + DA) 189 | 190 | RecSys 2024, [[PDF]](https://arxiv.org/pdf/2403.06372.pdf), [[Code]](https://github.com/KingGugu/RepPad) 191 | 192 | 36. **Rethinking sequential relationships: Improving sequential recommenders with inter-sequence data augmentation** (Sequential + DA) 193 | 194 | amazon.science 2024, [[PDF]](https://www.amazon.science/publications/rethinking-sequential-relationships-improving-sequential-recommenders-with-inter-sequence-data-augmentation) 195 | 196 | 37. **Beyond Relevance: Factor-level Causal Explanation for User Travel Decisions with Counterfactual Data Augmentation** (POI Rec + DA) 197 | 198 | TOIS 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3653673) 199 | 200 | 38. **TAU: Trajectory Data Augmentation with Uncertainty for Next POI Recommendation** (POI Rec + DA) 201 | 202 | AAAI 2024, [[PDF]](https://ojs.aaai.org/index.php/AAAI/article/download/30265/32257) 203 | 204 | 39. **Improving Long-Tail Item Recommendation with Graph Augmentation** (Graph + DA) 205 | 206 | CIKM 2023, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3583780.3614929) 207 | 208 | 40. **Improving Long-Tail Item Recommendation with Graph Augmentation** (Coupon Rec + DA) 209 | 210 | WWW 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3589335.3648306) 211 | 212 | 41. **Dataset Regeneration for Sequential Recommendation** (Sequential + DA) 213 | 214 | KDD 2024, [[PDF]](https://arxiv.org/pdf/2405.17795), [[Code]](https://anonymous.4open.science/r/KDD2024-86EA/) 215 | 216 | 42. **Counterfactual Data Augmentation for Debiased Coupon Recommendations Based on Potential Knowledge** (DA) 217 | 218 | WWW 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3589335.3648306) 219 | 220 | 43. **A Generic Behavior-Aware Data Augmentation Framework for Sequential Recommendation** (Sequential + DA) 221 | 222 | SIGIR 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3626772.3657682), [[Code]](https://github.com/XiaoJing-C/MBASR) 223 | 224 | 44. **Cross-reconstructed Augmentation for Dual-target Cross-domain Recommendation** (Cross-Domain + DA) 225 | 226 | SIGIR 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3626772.3657902), [[Code]](https://github.com/Double680/CrossAug) 227 | 228 | 45. **SCM4SR: Structural Causal Model-based Data Augmentation for Robust Session-based Recommendation** (Session Rec + DA) 229 | 230 | SIGIR 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3626772.3657940) 231 | 232 | 46. **GenRec: A Flexible Data Generator for Recommendations** (DA) 233 | 234 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2407.16594), [[Code]](https://anonymous.4open.science/r/GenRec-DED3) 235 | 236 | 47. **Sample Enrichment via Temporary Operations on Subsequences for Sequential Recommendation** (Sequential + DA) 237 | 238 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2407.17802), [[Code]](https://anonymous.4open.science/r/SETO-code-A026/) 239 | 240 | 48. **Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation** (POI Rec + DA) 241 | 242 | IJCAI 2021, [[PDF]](https://arxiv.org/abs/2106.15814) 243 | 244 | 49. **Federated Recommender System Based on Diffusion Augmentation and Guided Denoising** (Fed Rec + DA) 245 | 246 | TOIS 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3688570) 247 | 248 | 50. **Sliding Window Training - Utilizing Historical Recommender Systems Data for Foundation Models** (Sequential + DA) 249 | 250 | RecSys 2024, [[PDF]](https://arxiv.org/pdf/2409.14517) 251 | 252 | 51. **PACIFIC: Enhancing Sequential Recommendation via Preference-aware Causal Intervention and Counterfactual Data Augmentation** (Sequential + DA) 253 | 254 | CIKM 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3627673.3679803), [[Code]](https://github.com/ppppeanut/Pacific) 255 | 256 | 52. **Guided Diffusion-based Counterfactual Augmentation for Robust Session-based Recommendation** (Session + DA) 257 | 258 | RecSys 2024, [[PDF]](https://arxiv.org/pdf/2410.21892) 259 | 260 | 53. **Privacy-Preserving Synthetic Data Generation for Recommendation Systems** (Privacy-Preserving + DA) 261 | 262 | SIGIR 2022, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3477495.3532044), [[Code]](https://github.com/HuilinChenJN/UPC_SDG) 263 | 264 | 54. **Augmenting Sequential Recommendation with Balanced Relevance and Diversity** (Sequential + DA) 265 | 266 | AAAI 2025, [[PDF]](https://arxiv.org/pdf/2412.08300), [[Code]](https://github.com/KingGugu/BASRec) 267 | 268 | 55. **Temporal Linear Item-Item Model for Sequential Recommendation** (Sequential + DA) 269 | 270 | WSDM 2025, [[PDF]](https://arxiv.org/pdf/2412.07382), [[Code]](https://github.com/psm1206/TALE) 271 | 272 | 56. **One for Dozens: Adaptive REcommendation for All Domains with Counterfactual Augmentation** (Cross-Domain + DA) 273 | 274 | AAAI 2025, [[PDF]](https://arxiv.org/pdf/2412.11905), [[Code]](https://github.com/Chrissie-Law/AREAD-Multi-Domain-Recommendation) 275 | 276 | 57. **Generating Diverse Synthetic Datasets for Evaluation of Real-life Recommender Systems** (Evaluation + DA) 277 | 278 | RecSys 2024, [[PDF]](https://arxiv.org/pdf/2412.06809), [[Code]](https://github.com/outbrain-inc/outrank) 279 | 280 | 58. **CoMix: Collaborative Filtering with Mixup for Implicit Datasets** (CF + DA) 281 | 282 | INS 2023, [[PDF]](https://www.sciencedirect.com/science/article/pii/S0020025523001275) 283 | 284 | 59. **Batch-Mix Negative Sampling for Learning Recommendation Retrievers** (CF + DA) 285 | 286 | CIKM 2023, [[PDF]](https://dl.acm.org/doi/abs/10.1145/3583780.3614789) 287 | 288 | 60. **Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering** (CF + DA) 289 | 290 | CIKM 2023, [[PDF]](https://arxiv.org/pdf/2306.15905), [[Code]](https://github.com/Wu-Xi/DINS) 291 | 292 | 61. **LOAM: Improving Long-tail Session-based Recommendation via Niche Walk Augmentation and Tail Session Mixup** (Session Rec + DA) 293 | 294 | SIGIR 2023, [[PDF]](https://web.archive.org/web/20230720042142id_/https://dl.acm.org/doi/pdf/10.1145/3539618.3591718), [[Code]](https://github.com/yoony02/SIGIR-2023-LOAM) 295 | 296 | 62. **MixDec Sampling: A Soft Link-based Sampling Method of Graph Neural Network for Recommendation** (Graph + DA) 297 | 298 | ICMD 2022, [[PDF]](https://ieeexplore.ieee.org/abstract/document/10027691), [[Code]](https://github.com/a2093930/MixDec-Sampling) 299 | 300 | 63. **Domain Counterfactual Data Augmentation for Explainable Recommendation** (Explainable Rec + DA) 301 | 302 | TOIS 2025, [[PDF]](https://dl.acm.org/doi/abs/10.1145/3711856), [[Code]](https://github.com/yiyualt/D4C) 303 | 304 | 64. **Boosting Knowledge Graph-based Recommendations through Confidence-Aware Augmentation with Large Language Models** (Graph + DA) 305 | 306 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2502.03715) 307 | 308 | 65. **Leveraging Member–Group Relations via Multi-View Graph Filtering for Effective Group Recommendation** (Graph + DA) 309 | 310 | WWW 2025, [[PDF]](https://arxiv.org/pdf/2502.09050), [[Code]](https://github.com/chaehyun1/Group-GF) 311 | 312 | 66. **External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation** (Ads Rec + DA) 313 | 314 | WWW 2025, [[PDF]](https://arxiv.org/pdf/2502.17494) 315 | 316 | 67. **LLMSeR: Enhancing Sequential Recommendation via LLM-based Data Augmentation** (Sequential + LLM + DA) 317 | 318 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2503.12547) 319 | 320 | 68. **Boosting Factorization Machines via Saliency-Guided Mixup** (FM + DA) 321 | 322 | TPAMI 2024, [[PDF]](https://arxiv.org/pdf/2206.08661), [[Code]](https://github.com/Daftstone/SMFM) 323 | 324 | 69. **Data Augmentation as Free Lunch: Exploring the Test-Time Augmentation for Sequential Recommendation** (Sequential + DA) 325 | 326 | SIGIR 2025, [[PDF]](https://arxiv.org/pdf/2504.04843), [[Code]](https://github.com/KingGugu/TTA4SR) 327 | 328 | 70. **Improving Sequential Recommenders through Counterfactual Augmentation of System Exposure** (Sequential + DA) 329 | 330 | SIGIR 2025, [[PDF]](https://arxiv.org/pdf/2504.13482), [[Code]](https://github.com/ZiqiZhao1/CaseRec) 331 | 332 | 71. **SimAug: Enhancing Recommendation with Pretrained Language Models for Dense and Balanced Data Augmentation** (LLM + DA) 333 | 334 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2505.01695), [[Code]](https://github.com/YuyingZhao/SimAug) 335 | 336 | 337 | ## Graph Models with CL 338 | 339 | 1. **Self-supervised Graph Learning for Recommendation** (Graph + CL + DA) 340 | 341 | SIGIR 2021, [[PDF]](https://arxiv.org/pdf/2010.10783.pdf), [[Code]](https://github.com/wujcan/SGL-Torch) 342 | 343 | 2. **Contrastive Graph Structure Learning via Information Bottleneck for Recommendation** (Graph + CL) 344 | 345 | NeurIPS 2022, [[PDF]](https://openreview.net/pdf?id=lhl_rYNdiH6), [[Code]](https://github.com/weicy15/CGI) 346 | 347 | 3. **Are graph augmentations necessary? simple graph contrastive learning for recommendation** (Graph + CL) 348 | 349 | SIGIR 2022, [[PDF]](https://arxiv.org/pdf/2112.08679.pdf), [[Code]](https://github.com/Coder-Yu/SELFRec/blob/main/model/graph/SimGCL.py) 350 | 351 | 4. **XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation** (Graph + CL) 352 | 353 | TKDE 2022, [[PDF]](https://arxiv.org/pdf/2209.02544.pdf), [[Code]](https://github.com/Coder-Yu/SELFRec/blob/main/model/graph/XSimGCL.py) 354 | 355 | 5. **Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation** (Graph + CL + DA) 356 | 357 | arXiv 2022, [[PDF]](https://arxiv.org/pdf/2211.06370.pdf) 358 | 359 | 6. **DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation** (POI Rec, Graph + CL + DA) 360 | 361 | WSDM 2023, [[PDF]](https://arxiv.org/pdf/2210.16591.pdf), [[Code]](https://github.com/Fang6ang/DisenPOI) 362 | 363 | 7. **An MLP-based Algorithm for Efficient Contrastive Graph Recommendations** (Graph + CL + DA) 364 | 365 | SIGIR 2022, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3477495.3531874) 366 | 367 | 8. **A Review-aware Graph Contrastive Learning Framework for Recommendation** (Graph + CL + DA) 368 | 369 | SIGIR 2022, [[PDF]](https://arxiv.org/pdf/2204.12063.pdf), [[Code]](https://github.com/JarenceSJ/ReviewGraph) 370 | 371 | 9. **Simple Yet Effective Graph Contrastive Learning for Recommendation** (Graph + CL + DA) 372 | 373 | ICLR 2023, [[PDF]](https://arxiv.org/pdf/2302.08191.pdf), [[Code]](https://github.com/HKUDS/LightGCL) 374 | 375 | 10. **Contrastive Meta Learning with Behavior Multiplicity for Recommendation** (Graph + CL + DA) 376 | 377 | WSDM 2022, [[PDF]](https://arxiv.org/pdf/2202.08523.pdf), [[Code]](https://github.com/weiwei1206/CML) 378 | 379 | 11. **Disentangled Contrastive Learning for Social Recommendation** (Graph + CL + DA) 380 | 381 | CIKM 2022, [[PDF]](https://arxiv.org/pdf/2208.08723.pdf) 382 | 383 | 12. **Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning** (Graph + CL) 384 | 385 | CIKM 2022, [[PDF]](https://arxiv.org/pdf/2208.10061.pdf), [[Code]](https://github.com/CCIIPLab/KGIC) 386 | 387 | 13. **Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System** (Graph + CL) 388 | 389 | SIGIR 2022, [[PDF]](https://arxiv.org/pdf/2204.08807.pdf), [[Code]](https://github.com/CCIIPLab/MCCLK) 390 | 391 | 14. **Knowledge Graph Contrastive Learning for Recommendation** (Graph + DA + CL) 392 | 393 | SIGIR 2022, [[PDF]](https://arxiv.org/pdf/2205.00976.pdf), [[Code]](https://github.com/yuh-yang/KGCL-SIGIR22) 394 | 395 | 15. **Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation** (Graph + SSL) 396 | 397 | WWW 2021, [[PDF]](https://arxiv.org/pdf/2101.06448.pdf), [[Code]](https://github.com/Coder-Yu/QRec) 398 | 399 | 16. **SAIL: Self-Augmented Graph Contrastive Learning** (Graph + CL) 400 | 401 | AAAI 2022, [[PDF]](https://arxiv.org/pdf/2009.00934.pdf) 402 | 403 | 17. **Predictive and Contrastive: Dual-Auxiliary Learning for Recommendation** (Graph + CL) 404 | 405 | arXiv 2022, [[PDF]](https://arxiv.org/pdf/2203.03982.pdf) 406 | 407 | 18. **Socially-Aware Self-Supervised Tri-Training for Recommendation** (Graph + CL) 408 | 409 | KDD 2021, [[PDF]](https://arxiv.org/pdf/2106.03569.pdf), [[Code]](https://github.com/Coder-Yu/QRec) 410 | 411 | 19. **Predictive and Contrastive: Dual-Auxiliary Learning for Recommendation** (Graph + CL) 412 | 413 | arXiv 2022, [[PDF]](https://arxiv.org/pdf/2203.03982.pdf) 414 | 415 | 20. **Multi-Behavior Dynamic Contrastive Learning for Recommendation** (Graph + CL) 416 | 417 | arXiv 2022, [[PDF]](https://arxiv.org/pdf/2203.03982.pdf) 418 | 419 | 21. **Self-Augmented Recommendation with Hypergraph Contrastive Collaborative Filtering** (Graph + CL) 420 | 421 | SIGIR 2022, [[PDF]](https://arxiv.org/pdf/2204.12200), [[Code]](https://github.com/akaxlh/HCCF) 422 | 423 | 22. **Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning** (Graph + CF + CL) 424 | 425 | WWW 2022, [[PDF]](https://arxiv.org/pdf/2202.06200.pdf), [[Code]](https://github.com/RUCAIBox/NCL) 426 | 427 | 23. **Semi-deterministic and Contrastive Variational Graph Autoencoder for Recommendation** (Graph + CL) 428 | 429 | CIKM 2021, [[PDF]](https://dl.acm.org/doi/10.1145/3459637.3482390), [[Code]](https://github.com/syxkason/SCVG) 430 | 431 | 24. **Hypergraph Contrastive Collaborative Filtering** (Graph + CF + CL + DA) 432 | 433 | SIGIR 2022, [[PDF]](https://arxiv.org/pdf/2204.12200.pdf), [[Code]]( https://github.com/akaxlh/HCCF) 434 | 435 | 25. **Graph Structure Aware Contrastive Knowledge Distillation for Incremental Learning in Recommender Systems** (Graph + CL) 436 | 437 | CIKM 2021, [[PDF]](https://dl.acm.org/doi/10.1145/3459637.3482117), [[Code]](https://github.com/syxkason/SCVG) 438 | 439 | 26. **Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation** (Group Rec, Graph + CL + DA) 440 | 441 | CIKM 2021, [[PDF]](https://arxiv.org/abs/2109.04200), [[Code]](https://github.com/0411tony/HHGR) 442 | 443 | 27. **Self-Supervised Hypergraph Transformer for Recommender Systems** (Graph + SSL) 444 | 445 | KDD 2022, [[PDF]](https://arxiv.org/pdf/2207.14338.pdf), [[Code]](https://github.com/akaxlh/SHT) 446 | 447 | 28. **Episodes Discovery Recommendation with Multi-Source Augmentations** (Graph + DA + CL) 448 | 449 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2301.01737.pdf) 450 | 451 | 29. **Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation** (Graph + Sequential + CL) 452 | 453 | TOIS 2023, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3568395) 454 | 455 | 30. **Adversarial Learning Data Augmentation for Graph Contrastive Learning in Recommendation** (Graph + DA + CL) 456 | 457 | DASFAA 2023, [[PDF]](https://arxiv.org/pdf/2302.02317.pdf) 458 | 459 | 31. **SimCGNN: Simple Contrastive Graph Neural Network for Session-based Recommendation** (Graph + CL) 460 | 461 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2302.03997.pdf) 462 | 463 | 32. **MA-GCL: Model Augmentation Tricks for Graph Contrastive Learning** (Graph + DA + CL) 464 | 465 | AAAI 2023, [[PDF]](https://arxiv.org/pdf/2212.07035.pdf), [[Code]](https://github.com/GXM1141/MA-GCL) 466 | 467 | 33. **Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation** (Graph + Session + CL) 468 | 469 | AAAI 2021, [[PDF]](https://arxiv.org/pdf/2012.06852.pdf), [[Code]](https://github.com/xiaxin1998/DHCN) 470 | 471 | 34. **Self-Supervised Graph Co-Training for Session-based Recommendation** (Graph + Session + CL) 472 | 473 | CIMK 2021, [[PDF]](https://arxiv.org/pdf/2108.10560.pdf), [[Code]](https://github.com/xiaxin1998/COTREC) 474 | 475 | 35. **Heterogeneous Graph Contrastive Learning for Recommendation** (Graph + CL) 476 | 477 | WSDM 2023, [[PDF]](https://arxiv.org/pdf/2303.00995.pdf), [[Code]](https://github.com/HKUDS/HGCL) 478 | 479 | 36. **Automated Self-Supervised Learning for Recommendation** (Graph + DA + CL) 480 | 481 | WWW 2023, [[PDF]](https://arxiv.org/pdf/2303.07797.pdf), [[Code]](https://github.com/HKUDS/AutoCF) 482 | 483 | 37. **Graph-less Collaborative Filtering** (Graph + CL) 484 | 485 | WWW 2023, [[PDF]](https://arxiv.org/pdf/2303.08537.pdf), [[Code]](https://github.com/HKUDS/SimRec) 486 | 487 | 38. **Disentangled Contrastive Collaborative Filtering** (Graph + CL) 488 | 489 | SIGIR 2023, [[PDF]](https://arxiv.org/pdf/2305.02759.pdf), [[Code]](https://github.com/HKUDS/DCCF) 490 | 491 | 39. **Knowledge-refined Denoising Network for Robust Recommendation** (Graph + CL) 492 | 493 | SIGIR 2023, [[PDF]](https://arxiv.org/pdf/2304.14987.pdf), [[Code]](https://github.com/xj-zhu98/KRDN) 494 | 495 | 40. **Disentangled Graph Contrastive Learning for Review-based Recommendation** (Graph + CL) 496 | 497 | arxiv 2022, [[PDF]](https://arxiv.org/pdf/2209.01524.pdf) 498 | 499 | 41. **Adaptive Graph Contrastive Learning for Recommendation** (Graph + CL) 500 | 501 | SIGIR 2023, [[PDF]](https://arxiv.org/abs/2305.10837), [[Code]](https://github.com/ZzMeei/AdaptiveGCL) 502 | 503 | 42. **Knowledge Enhancement for Contrastive Multi-Behavior Recommendation** (Graph + CL) 504 | 505 | WSDM 2023, [[PDF]](https://arxiv.org/pdf/2301.05403.pdf), [[Code]](https://github.com/HKUDS/SSLRec) 506 | 507 | 43. **Contrastive Meta Learning with Behavior Multiplicity for Recommendation** (Graph + CL) 508 | 509 | WSDM 2022, [[PDF]](https://arxiv.org/pdf/2202.08523.pdf), [[Code]](https://github.com/weiwei1206/CML) 510 | 511 | 44. **Graph Transformer for Recommendation** (Graph + CL) 512 | 513 | SIGIR 2023, [[PDF]](https://arxiv.org/pdf/2306.02330.pdf), [[Code]](https://github.com/HKUDS/GFormer) 514 | 515 | 45. **PANE-GNN: Unifying Positive and Negative Edges in Graph Neural Networks for Recommendation** (Graph + CL) 516 | 517 | arxiv 2023, [[PDF]](https://arxiv.org/pdf/2306.04095.pdf) 518 | 519 | 46. **Knowledge Graph Self-Supervised Rationalization for Recommendation** (Graph + CL) 520 | 521 | KDD 2023, [[PDF]](https://arxiv.org/pdf/2307.02759.pdf), [[Code]](https://github.com/HKUDS/KGRec) 522 | 523 | 47. **Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization** (Graph + CL) 524 | 525 | SIGIR 2021, [[PDF]](https://dl.acm.org/doi/abs/10.1145/3404835.3462928) 526 | 527 | 48. **Generative-Contrastive Graph Learning for Recommendation** (Graph + CL) 528 | 529 | SIGIR 2023, [[PDF]](https://arxiv.org/pdf/2307.05100.pdf) 530 | 531 | 49. **AdaMCL: Adaptive Fusion Multi-View Contrastive Learning for Collaborative Filtering** (Graph + CL) 532 | 533 | SIGIR 2023, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3539618.3591632), [[Code]](https://github.com/PasaLab/AdaMCL) 534 | 535 | 50. **Candidate–aware Graph Contrastive Learning for Recommendation** (Graph + CL) 536 | 537 | SIGIR 2023, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3539618.3591647), [[Code]](https://github.com/WeiHeCnSH/CGCL-Pytorch-master) 538 | 539 | 51. **Multi-View Graph Convolutional Network for Multimedia Recommendation** (Graph + CL) 540 | 541 | MM 2023, [[PDF]](https://arxiv.org/ftp/arxiv/papers/2308/2308.03588.pdf), [[Code]](https://github.com/demonph10/MGCN) 542 | 543 | 52. **Uncertainty-aware Consistency Learning for Cold-Start Item Recommendation** (Graph + CL) 544 | 545 | SIGIR 2023, [[PDF]](https://arxiv.org/pdf/2308.03470.pdf) 546 | 547 | 53. **uCTRL: Unbiased Contrastive Representation Learning via Alignment and Uniformity for Collaborative Filtering** (Graph + CL) 548 | 549 | SIGIR 2023, [[PDF]](https://arxiv.org/pdf/2305.12768.pdf), [[Code]](https://github.com/Jaewoong-Lee/sigir_2023_uCTRL) 550 | 551 | 54. **Contrastive Box Embedding for Collaborative Reasoning** (Graph + CL) 552 | 553 | SIGIR 2023, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3539618.3591654) 554 | 555 | 55. **Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph** (Graph + CL) 556 | 557 | CIKM 2023, [[PDF]](https://arxiv.org/pdf/2308.07752.pdf) 558 | 559 | 56. **Contrastive Graph Prompt-tuning for Cross-domain Recommendation** (Graph + CL) 560 | 561 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2308.10685.pdf) 562 | 563 | 57. **Dual Intents Graph Modeling for User-centric Group Discovery** (Graph + CL) 564 | 565 | CIKM 2023, [[PDF]](https://arxiv.org/pdf/2308.05013.pdf), [[Code]](https://github.com/WxxShirley/CIKM2023DiRec) 566 | 567 | 58. **Group Identification via Transitional Hypergraph Convolution with Cross-view Self-supervised Learning** (Graph + CL) 568 | 569 | CIKM 2023, [[PDF]](https://arxiv.org/pdf/2308.08620.pdf), [[Code]](https://github.com/mdyfrank/GTGS) 570 | 571 | 59. **Multi-Relational Contrastive Learning for Recommendation** (Graph + CL) 572 | 573 | RecSys 2023, [[PDF]](https://arxiv.org/pdf/2309.01103.pdf), [[Code]](https://github.com/HKUDS/RCL) 574 | 575 | 60. **Multi-behavior Recommendation with SVD Graph Neural Networks** (Graph + CL) 576 | 577 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2309.06912.pdf) 578 | 579 | 61. **E-commerce Search via Content Collaborative Graph Neural Network** (Graph + DA + CL) 580 | 581 | KDD 2023, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3580305.3599320), [[Code]](https://github.com/XMUDM/CC-GNN) 582 | 583 | 62. **Long-tail Augmented Graph Contrastive Learning for Recommendation** (Graph + DA + CL) 584 | 585 | PKDD 2023, [[PDF]](https://arxiv.org/pdf/2309.11177.pdf), [[Code]](https://github.com/im0qianqian/LAGCL) 586 | 587 | 63. **LMACL: Improving Graph Collaborative Filtering with Learnable Model Augmentation Contrastive Learning** (Graph + CL) 588 | 589 | TKDD 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3657302), [[Code]](https://github.com/LiuHsinx/LMACL) 590 | 591 | 64. **On the Sweet Spot of Contrastive Views for Knowledge-enhanced Recommendation** (Graph + CL) 592 | 593 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2309.13384.pdf), [[Code]](https://figshare.com/articles/conference_contribution/SimKGCL/22783382) 594 | 595 | 65. **Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering** (Graph + CL) 596 | 597 | TKDE 2023, [[PDF]](https://ieeexplore.ieee.org/document/10255367), [[Code]](https://gitee.com/peijie_hfut/nescl) 598 | 599 | 66. **Towards Robust Neural Graph Collaborative Filtering via Structure Denoising and Embedding Perturbation** (Graph + CL) 600 | 601 | TOIS 2023, [[PDF]](https://dl.acm.org/doi/full/10.1145/3568396) 602 | 603 | 67. **TDCGL: Two-Level Debiased Contrastive Graph Learning for Recommendation** (Graph + CL) 604 | 605 | arXiv 2023, [[PDF]](https://browse.arxiv.org/pdf/2310.00569.pdf) 606 | 607 | 68. **Topology-aware Debiased Self-supervised Graph Learning for Recommendation** (Graph + CL) 608 | 609 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2310.15858.pdf), [[Code]](https://github.com/malajikuai/TDSGL) 610 | 611 | 69. **Learning to Denoise Unreliable Interactions for Graph Collaborative Filtering** (Graph + DA + CL) 612 | 613 | SIGIR 2022, [[PDF]](https://dl.acm.org/doi/abs/10.1145/3477495.3531889), [[Code]](https://github.com/ChangxinTian/RGCF) 614 | 615 | 70. **Contrastive Multi-Level Graph Neural Networks for Session-based Recommendation** (Graph + CL) 616 | 617 | TMM 2023, [[PDF]](https://arxiv.org/pdf/2311.02938.pdf) 618 | 619 | 71. **An Empirical Study Towards Prompt-Tuning for Graph Contrastive Pre-Training in Recommendations** (Graph + CL) 620 | 621 | NeurIPS 2023, [[PDF]](https://openreview.net/pdf?id=XyAP8ScqLV), [[Code]](https://github.com/Haoran-Young/CPTPP) 622 | 623 | 72. **An Empirical Study Towards Prompt-Tuning for Graph Contrastive Pre-Training in Recommendations** (Graph + CL) 624 | 625 | ICDM 2021, [[PDF]](https://ieeexplore.ieee.org/document/9678992), [[Code]](https://github.com/Haoran-Young/HMG-CR) 626 | 627 | 73. **Denoised Self-Augmented Learning for Social Recommendation** (Graph + CL) 628 | 629 | IJCAI 2023, [[PDF]](https://arxiv.org/pdf/2305.12685.pdf), [[Code]](https://github.com/HKUDS/DSL) 630 | 631 | 74. **Intent-aware Recommendation via Disentangled Graph Contrastive Learning** (Graph + CL) 632 | 633 | IJCAI 2023, [[PDF]](https://www.ijcai.org/proceedings/2023/0260.pdf) 634 | 635 | 75. **GENET: Unleashing the Power of Side Information for Recommendation via Hypergraph Pre-training** (Graph + CL) 636 | 637 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2311.13121.pdf) 638 | 639 | 76. **Graph Pre-training and Prompt Learning for Recommendation** (Graph + CL) 640 | 641 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2311.16716.pdf) 642 | 643 | 77. **Robust Basket Recommendation via Noise-tolerated Graph Contrastive Learning** (Graph + CL) 644 | 645 | CIKM 2023, [[PDF]](https://arxiv.org/pdf/2311.16334.pdf), [[Code]](https://github.com/Xinrui17/BNCL) 646 | 647 | 78. **ID Embedding as Subtle Features of Content and Structure for Multimodal Recommendation** (Graph + Multi-Modal + CL) 648 | 649 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2311.05956.pdf) 650 | 651 | 79. **Knowledge Graphs and Pre-trained Language Models enhanced Representation Learning for Conversational Recommender Systems** (Graph + LLM + CL) 652 | 653 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2312.10967.pdf) 654 | 655 | 80. **LGMRec: Local and Global Graph Learning for Multimodal Recommendation** (Graph + Multi-Modal + CL) 656 | 657 | AAAI 2024, [[PDF]](https://arxiv.org/pdf/2312.16400.pdf), [[Code]](https://github.com/georgeguo-cn/LGMRec) 658 | 659 | 81. **RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation** (Graph + CL) 660 | 661 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2312.16563.pdf) 662 | 663 | 82. **DiffKG: Knowledge Graph Diffusion Model for Recommendation** (Graph + CL) 664 | 665 | WSDM 2024, [[PDF]](https://arxiv.org/pdf/2312.16890.pdf), [[Code]](https://github.com/HKUDS/DiffKG) 666 | 667 | 83. **QoS-Aware Graph Contrastive Learning for Web Service Recommendation** (Graph + CL) 668 | 669 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2401.03162.pdf) 670 | 671 | 84. **Challenging Low Homophily in Social Recommendation** (Graph + CL) 672 | 673 | WWW 2024, [[PDF]](https://arxiv.org/pdf/2401.14606.pdf) 674 | 675 | 85. **RecDCL: Dual Contrastive Learning for Recommendation** (Graph + CL) 676 | 677 | WWW 2024, [[PDF]](https://arxiv.org/pdf/2401.15635.pdf), [[Code]](https://github.com/THUDM/RecDCL) 678 | 679 | 86. **Prerequisite-Enhanced Category-Aware Graph Neural Networks for Course Recommendation** (Graph + CL) 680 | 681 | TKDD 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3643644) 682 | 683 | 87. **Graph Contrastive Learning With Negative Propagation for Recommendation** (Graph + CL) 684 | 685 | TCSS 2024, [[PDF]](https://ieeexplore.ieee.org/abstract/document/10419035) 686 | 687 | 88. **General Debiasing for Graph-based Collaborative Filtering via Adversarial Graph Dropout** (Graph + CL) 688 | 689 | WWW 2024, [[PDF]](https://arxiv.org/pdf/2402.13769.pdf), [[Code]](https://github.com/Arthurma71/AdvDrop) 690 | 691 | 89. **Breaking the Barrier: Utilizing Large Language Models for Industrial Recommendation Systems through an Inferential Knowledge Graph** (Graph + CL) 692 | 693 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2402.13750.pdf) 694 | 695 | 90. **FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling** (Graph + CL) 696 | 697 | SIAM 2024, [[PDF]](https://arxiv.org/pdf/2403.02630.pdf), [[Code]](https://github.com/orion-orion/FedHCDR) 698 | 699 | 91. **Self-supervised Contrastive Learning for Implicit Collaborative Filtering** (Graph + DA + CL) 700 | 701 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2403.07265.pdf) 702 | 703 | 92. **Dual-Channel Multiplex Graph Neural Networks for Recommendation** (Graph + CL) 704 | 705 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2403.11624.pdf) 706 | 707 | 93. **Bilateral Unsymmetrical Graph Contrastive Learning for Recommendation** (Graph + CL) 708 | 709 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2403.15075.pdf) 710 | 711 | 94. **Knowledge-aware Dual-side Attribute-enhanced Recommendation** (Graph + CL) 712 | 713 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2403.16037.pdf), [[Code]](https://github.com/TJTP/KDAR) 714 | 715 | 95. **A Progressively-Passing-then-Disentangling Approach to Recipe Recommendation** (Graph + CL) 716 | 717 | TMM 2024, [[PDF]](https://ieeexplore.ieee.org/abstract/document/10460165/) 718 | 719 | 96. **Graph Augmentation for Recommendation** (Graph + DA + CL) 720 | 721 | ICDE 2024, [[PDF]](https://arxiv.org/pdf/2403.16656.pdf), [[Code]](https://github.com/HKUDS/GraphAug) 722 | 723 | 97. **One Backpropagation in Two Tower Recommendation Models** (Graph + CL) 724 | 725 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2403.18227.pdf) 726 | 727 | 98. **Improving Content Recommendation: Knowledge Graph-Based Semantic Contrastive Learning for Diversity and Cold-Start Users** (Graph + CL) 728 | 729 | COLING 2024, [[PDF]](https://arxiv.org/pdf/2403.18667.pdf) 730 | 731 | 99. **Dual Homogeneity Hypergraph Motifs with Cross-view Contrastive Learning for Multiple Social Recommendations** (Graph + Social Rec + CL) 732 | 733 | TKDD 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3653976), [[Code]](https://github.com/chenai2024/DH-HGCNplusplus) 734 | 735 | 100. **Graph Disentangled Contrastive Learning with Personalized Transfer for Cross-Domain Recommendation** (Graph + CL) 736 | 737 | AAAI 2024, [[PDF]](https://ojs.aaai.org/index.php/AAAI/article/download/28723/29398) 738 | 739 | 101. **A Directional Diffusion Graph Transformer for Recommendation** (Graph + CL) 740 | 741 | SIGIR 2024, [[PDF]](https://arxiv.org/pdf/2404.03326.pdf) 742 | 743 | 102. **Heterogeneous Adaptive Preference Learning for Recommendation** (Graph + CL) 744 | 745 | TORS 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3656480), [[Code]](https://github.com/Feifei84/HAPLRec/tree/main/) 746 | 747 | 103. **Stock Recommendations for Individual Investors: A Temporal Graph Network Approach with Diversification-Enhancing Contrastive Learning** (Graph + CL) 748 | 749 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2404.07223.pdf), [[Code]](https://anonymous.4open.science/r/IJCAI2024-12F4) 750 | 751 | 104. **Disentangled Cascaded Graph Convolution Networks for Multi-Behavior Recommendation** (Graph + CL) 752 | 753 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2404.11519.pdf), [[Code]](https://github.com/JianhuaDongCS/Disen-CGCN) 754 | 755 | 105. **Enhanced Hierarchical Contrastive Learning for Recommendation** (Graph + CL) 756 | 757 | AAAI 2024, [[PDF]](https://ojs.aaai.org/index.php/AAAI/article/download/28761/29461) 758 | 759 | 106. **How to Improve Representation Alignment and Uniformity in Graph-based Collaborative Filtering?** (Graph + CL) 760 | 761 | AAAI 2024, [[PDF]](https://zyouyang.github.io/assets/publications/AUPlus.pdf), [[Code]](https://github.com/zyouyang/AUPlus) 762 | 763 | 107. **PopDCL: Popularity-aware Debiased Contrastive Loss for Collaborative Filtering** (Graph + CL) 764 | 765 | CIKM 2023, [[PDF]](https://www.researchgate.net/profile/Liu-Zhuang/publication/374907265_PopDCL_Popularity-aware_Debiased_Contrastive_Loss_for_Collaborative_Filtering/links/65dda61ce7670d36abe2b0eb/PopDCL-Popularity-aware-Debiased-Contrastive-Loss-for-Collaborative-Filtering.pdf) 766 | 767 | 108. **Improving Graph Collaborative Filtering with Directional Behavior Enhanced Contrastive Learning** (Graph + CL) 768 | 769 | TKDD 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3663574) 770 | 771 | 109. **SCONE: A Novel Stochastic Sampling to Generate Contrastive Views and Hard Negative Samples for Recommendation** (Graph + CL) 772 | 773 | WSDM 2025, [[PDF]](https://arxiv.org/pdf/2405.00287), [[Code]](https://github.com/jeongwhanchoi/SCONE) 774 | 775 | 110. **Learning Social Graph for Inactive User Recommendation** (Graph + CL) 776 | 777 | DASFAA 2024, [[PDF]](https://arxiv.org/pdf/2405.05288), [[Code]](https://github.com/liun-online/LSIR) 778 | 779 | 111. **Exploring the Individuality and Collectivity of Intents behind Interactions for Graph Collaborative Filtering** (Graph + CL) 780 | 781 | SIGIR 2024, [[PDF]](https://arxiv.org/pdf/2405.09042), [[Code]](https://github.com/BlueGhostYi/BIGCF) 782 | 783 | 112. **Graph Contrastive Learning with Kernel Dependence Maximization for Social Recommendation** (Graph + CL) 784 | 785 | WWW 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3589334.3645412) 786 | 787 | 113. **MvStHgL: Multi-view Hypergraph Learning with Spatial-temporal Periodic Interests for Next POI Recommendation** (Graph + POI Rec + CL) 788 | 789 | TOIS 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3664651) 790 | 791 | 114. **A Vlogger-augmented Graph Neural Network Model for Micro-video Recommendation** (Graph + CL) 792 | 793 | ECML-PKDD 2023, [[PDF]](https://arxiv.org/pdf/2405.18260), [[Code]](https://github.com/laiweijiang/VAGNN) 794 | 795 | 115. **Knowledge Enhanced Multi-intent Transformer Network for Recommendation** (Graph + CL) 796 | 797 | WWW 2024, [[PDF]](https://arxiv.org/pdf/2405.20565), [[Code]](https://github.com/CCIIPLab/KGTN) 798 | 799 | 116. **QAGCF: Graph Collaborative Filtering for Q&A Recommendation** (Graph + CL) 800 | 801 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2406.04828) 802 | 803 | 117. **Balancing Embedding Spectrum for Recommendation** (Graph + CL) 804 | 805 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2406.12032), [[Code]](https://github.com/tanatosuu/directspec) 806 | 807 | 118. **Meta Graph Learning for Long-tail Recommendation** (Graph + CL) 808 | 809 | KDD 2023, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3580305.3599428), [[Code]](https://github.com/weicy15/MGL) 810 | 811 | 119. **Heterogeneous Hypergraph Embedding for Recommendation Systems** (Graph + CL) 812 | 813 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2407.03665), [[Code]](https://github.com/viethungvu1998/KHGRec) 814 | 815 | 120. **Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for Recommendations** (Graph + CL) 816 | 817 | KDD 2024, [[PDF]](https://arxiv.org/pdf/2407.05126), [[Code]](https://github.com/foodfaust/CDR) 818 | 819 | 121. **Towards Robust Recommendation via Decision Boundary-aware Graph Contrastive Learning** (Graph + CL) 820 | 821 | KDD 2024, [[PDF]](https://arxiv.org/pdf/2407.10184), [[Code]](https://cl4rec.github.io/RGCL) 822 | 823 | 122. **Graph Augmentation Empowered Contrastive Learning for Recommendation** (Graph + DA + CL) 824 | 825 | TOIS 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3677377) 826 | 827 | 123. **L2CL: Embarrassingly Simple Layer-to-Layer Contrastive Learning for Graph Collaborative Filtering** (Graph + CL) 828 | 829 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2407.14266), [[Code]](https://github.com/downeykking/L2CL) 830 | 831 | 124. **RevGNN: Negative Sampling Enhanced Contrastive Graph Learning for Academic Reviewer Recommendation** (Graph + CL) 832 | 833 | TOIS 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3679200), [[Code]](https://github.com/THUDM/Reviewer-Rec) 834 | 835 | 125. **Intent-Guided Heterogeneous Graph Contrastive Learning for Recommendation** (Graph + CL) 836 | 837 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2407.17234), [[Code]](https://github.com/wangyu0627/IHGCL) 838 | 839 | 126. **Your Graph Recommender is Provably a Single-view Graph Contrastive Learning** (Graph + CL) 840 | 841 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2407.17723) 842 | 843 | 127. **High-Order Fusion Graph Contrastive Learning for Recommendation** (Graph + CL) 844 | 845 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2407.19692) 846 | 847 | 128. **Feedback Reciprocal Graph Collaborative Filtering** (Graph + CL) 848 | 849 | CIKM 2024, [[PDF]](https://arxiv.org/pdf/2408.02404) 850 | 851 | 129. **Symmetric Graph Contrastive Learning against Noisy Views for Recommendation** (Graph + CL) 852 | 853 | TOIS 2025, [[PDF]](https://arxiv.org/pdf/2408.02691), [[Code]](https://github.com/user683/SGCL) 854 | 855 | 130. **Dual-Channel Latent Factor Analysis Enhanced Graph Contrastive Learning for Recommendation** (Graph + DA + CL) 856 | 857 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2408.04838) 858 | 859 | 131. **Meta-optimized Structural and Semantic Contrastive Learning for Graph Collaborative Filtering** (Graph + DA + CL) 860 | 861 | ICDE 2024, [[PDF]](https://ieeexplore.ieee.org/abstract/document/10597955), [[Code]](https://github.com/YongjingHao/Meta-SSCL) 862 | 863 | 132. **Unveiling Vulnerabilities of Contrastive Recommender Systems to Poisoning Attacks** (Graph + Attack + CL) 864 | 865 | KDD 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3637528.3671795), [[Code]](https://github.com/CoderWZW/ARLib) 866 | 867 | 133. **Enhancing Graph Contrastive Learning with Reliable and Informative Augmentation for Recommendation** (Graph + CL) 868 | 869 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2409.05633) 870 | 871 | 134. **Multi-view Hypergraph-based Contrastive Learning Model for Cold-Start Micro-video Recommendation** (Graph + CL) 872 | 873 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2409.09638) 874 | 875 | 135. **TwinCL: A Twin Graph Contrastive Learning Model for Collaborative Filtering** (Graph + CL) 876 | 877 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2409.19169), [[Code]](https://github.com/chengkai-liu/TwinCL) 878 | 879 | 136. **Firzen: Firing Strict Cold-Start Items with Frozen Heterogeneous and Homogeneous Graphs for Recommendation** (Graph + CL) 880 | 881 | ICDE 2024, [[PDF]](https://arxiv.org/pdf/2410.07654), [[Code]](https://github.com/PKU-ICST-MIPL/Firzen_ICDE2024) 882 | 883 | 137. **Firzen: Firing Strict Cold-Start Items with Frozen Heterogeneous and Homogeneous Graphs for Recommendation** (Graph + CL) 884 | 885 | ICWS 2024, [[PDF]](https://arxiv.org/pdf/2410.10296), [[Code]](https://github.com/ItsukiFujii/AttrGAU) 886 | 887 | 138. **Adaptive Fusion of Multi-View for Graph Contrastive Recommendation** (Graph + DA + CL) 888 | 889 | RecSys 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3640457.3688153), [[Code]](https://github.com/Du-danger/AMGCR) 890 | 891 | 139. **Simplify to the Limit! Embedding-less Graph Collaborative Filtering for Recommender Systems** (Graph + CL) 892 | 893 | TOIS 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3701230), [[Code]](https://github.com/BlueGhostYi/ID-GRec) 894 | 895 | 140. **FairDgcl: Fairness-aware Recommendation with Dynamic Graph Contrastive Learning** (Graph + DA + CL) 896 | 897 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2410.17555), [[Code]](https://github.com/cwei01/FairDgcl) 898 | 899 | 141. **Decoupled Behavior-based Contrastive Recommendation** (Graph + CL) 900 | 901 | CIKM 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3627673.3679636), [[Code]](https://github.com/Du-danger/DBCR) 902 | 903 | 142. **Mixed Supervised Graph Contrastive Learning for Recommendation** (Graph + DA + CL) 904 | 905 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2404.15954) 906 | 907 | 143. **Bi-Level Graph Structure Learning for Next POI Recommendation** (Graph + POI Rec + CL) 908 | 909 | TKDE 2024, [[PDF]](https://arxiv.org/pdf/2411.01169) 910 | 911 | 144. **Bi-Level Graph Structure Learning for Next POI Recommendation** (Graph + Multi-Modal + CL) 912 | 913 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2411.01169) 914 | 915 | 145. **Mitigating Matthew Effect: Multi-Hypergraph Boosted Multi-Interest Self-Supervised Learning for Conversational Recommendation** (Graph + CL) 916 | 917 | EMNLP 2024, [[PDF]](https://aclanthology.org/2024.emnlp-main.86.pdf), [[Code]](https://github.com/zysensmile/HiCore) 918 | 919 | 146. **Unifying Graph Convolution and Contrastive Learning in Collaborative Filtering** (Graph + CL) 920 | 921 | KDD 2024, [[PDF]](https://arxiv.org/pdf/2406.13996), [[Code]](https://github.com/wu1hong/SCCF) 922 | 923 | 147. **DeBaTeR: Denoising Bipartite Temporal Graph for Recommendation** (Graph + CL) 924 | 925 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2411.09181) 926 | 927 | 148. **Next Point-of-Interest Recommendation with Adaptive Graph Contrastive Learning** (Graph + CL) 928 | 929 | TKDE 2024, [[PDF]](https://ieeexplore.ieee.org/document/10772008) 930 | 931 | 149. **Graph-Sequential Alignment and Uniformity: Toward Enhanced Recommendation Systems** (Graph + Sequential + CL) 932 | 933 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2412.04276), [[Code]](https://github.com/YuweiCao-UIC/GSAU) 934 | 935 | 150. **Multi-Graph Co-Training for Capturing User Intent in Session-based Recommendation** (Graph + DA + CL) 936 | 937 | COLING 2025, [[PDF]](https://arxiv.org/pdf/2412.11105), [[Code]](https://github.com/liang-tian-tian/MGCOT) 938 | 939 | 151. **SPGL: Enhancing Session-based Recommendation with Single Positive Graph Learning** (Graph + CL) 940 | 941 | ICONIP 2024, [[PDF]](https://arxiv.org/pdf/2412.11846), [[Code]](https://github.com/liang-tian-tian/SPGL) 942 | 943 | 152. **Heterogeneous Graph Collaborative Filtering** (Graph + DA + CL) 944 | 945 | WSDM 2025, [[PDF]](https://arxiv.org/pdf/2412.13825), [[Code]](https://github.com/HKUDS/MixRec) 946 | 947 | 153. **DisCo: Graph-Based Disentangled Contrastive Learning for Cold-Start Cross-Domain Recommendation** (Graph + CL) 948 | 949 | AAAI 2025, [[PDF]](https://arxiv.org/pdf/2412.15005), [[Code]](https://github.com/HourunLi/2025-AAAI-DisCo) 950 | 951 | 154. **Spectrum-based Modality Representation Fusion Graph Convolutional Network for Multimodal Recommendation** (Graph + Multi-Modal + CL) 952 | 953 | WSDM 2025, [[PDF]](https://arxiv.org/pdf/2412.14978), [[Code]](https://github.com/kennethorq/SMORE) 954 | 955 | 155. **HEC-GCN: Hypergraph Enhanced Cascading Graph Convolution Network for Multi-Behavior Recommendation** (Graph + CL) 956 | 957 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2412.14476), [[Code]](https://github.com/marqu22/HEC-GCN) 958 | 959 | 156. **Heterogeneous Hyperbolic Hypergraph Neural Network for Friend Recommendation in Location-based Social Networks** (Graph + CL) 960 | 961 | TKDD 2024, [[PDF]](https://arxiv.org/pdf/2412.14476), [[Code]](https://github.com/liyongkang123/H3GNN) 962 | 963 | 157. **Score-based Generative Diffusion Models for Social Recommendations** (Graph + CL) 964 | 965 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2412.15579), [[Code]](https://github.com/Anonymous-CodeRepository/Score-based-Generative-Diffusion-Models-for-Social-Recommendations-SGSR) 966 | 967 | 158. **Hyperbolic Graph Contrastive Learning for Collaborative Filtering** (Graph + DA + CL) 968 | 969 | TKDE 2024, [[PDF]](https://ieeexplore.ieee.org/document/10816511) 970 | 971 | 159. **Efficient Session-based Recommendation with Contrastive Graph-based Shortest Path Search** (Graph + CL) 972 | 973 | TORS 2024, [[PDF]](https://dl.acm.org/doi/10.1145/3701764), [[Code]](https://github.com/dbis-uibk/SPARE) 974 | 975 | 160. **Don’t Lose Yourself: Boosting Multimodal Recommendation via Reducing Node-neighbor Discrepancy in Graph Convolutional Network** (Graph + CL) 976 | 977 | ICASSP 2025, [[PDF]](https://arxiv.org/pdf/2412.18962) 978 | 979 | 161. **Multi-behavior Hypergraph Contrastive Learning for Session-based Recommendation** (Graph + CL) 980 | 981 | TKDE 2024, [[PDF]](https://ieeexplore.ieee.org/abstract/document/10816604) 982 | 983 | 162. **DiffCL: A Diffusion-Based Contrastive Learning Framework with Semantic Alignment for Multimodal Recommendations** (Graph + Multi-Modal + CL) 984 | 985 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2501.01066) 986 | 987 | 163. **Pone-GNN: Integrating Positive and Negative Feedback in Graph Neural Networks for Recommender Systems** (Graph + CL) 988 | 989 | TORS 2025, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3711666), [[Code]](https://github.com/Young0222/Pone-GNN) 990 | 991 | 164. **LightGNN: Simple Graph Neural Network for Recommendation** (Graph + DA + CL) 992 | 993 | WSDM 2025, [[PDF]](https://arxiv.org/pdf/2501.03228), [[Code]](https://github.com/HKUDS/LightGNN) 994 | 995 | 165. **Graph Contrastive Learning on Multi-label Classification for Recommendations** (Graph + DA + CL) 996 | 997 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2501.06985) 998 | 999 | 166. **Graph Contrastive Learning on Multi-label Classification for Recommendations** (Graph + DA + CL) 1000 | 1001 | NN 2025, [[PDF]](https://www.sciencedirect.com/science/article/pii/S0893608025000243) 1002 | 1003 | 167. **A Contrastive Framework with User, Item and Review Alignment for Recommendation** (Graph + CL) 1004 | 1005 | WSDM 2025, [[PDF]](https://arxiv.org/pdf/2501.11963) 1006 | 1007 | 168. **A Contrastive Framework with User, Item and Review Alignment for Recommendation** (Graph + DA + CL) 1008 | 1009 | WWW 2025, [[PDF]](https://arxiv.org/pdf/2501.13579), [[Code]](https://github.com/BlueGhostYi/ID-GRec) 1010 | 1011 | 169. **Disentangled Multi-Graph Convolution for Cross-Domain Recommendation** (Graph + CL) 1012 | 1013 | TKDD 2025, [[PDF]](https://dl.acm.org/doi/10.1145/3715151) 1014 | 1015 | 170. **Perturbation-driven Dual Auxiliary Contrastive Learning for Collaborative Filtering Recommendation** (Graph + DA + CL) 1016 | 1017 | COLING 2025, [[PDF]](https://aclanthology.org/2025.coling-main.44/), [[Code]](https://github.com/zky77/PDACL) 1018 | 1019 | 171. **Intent-guided Heterogeneous Graph Contrastive Learning for Recommendation** (Graph + DA + CL) 1020 | 1021 | TKDE 2025, [[PDF]](https://ieeexplore.ieee.org/document/10857594), [[Code]](https://github.com/wangyu0627/IHGCL) 1022 | 1023 | 172. **TPGRec: Text-Enhanced and Popularity-Smoothing Graph Collaborative Filtering for Long-Tail Item Recommendation** (Graph + CL) 1024 | 1025 | Neurocomputing 2025, [[PDF]](https://www.sciencedirect.com/science/article/pii/S0925231225002115), [[Code]](https://github.com/ycy89/MyTPGRec) 1026 | 1027 | 173. **Hypergraph Collaborative Filtering with Adaptive Augmentation of Graph Data for Recommendation** (Graph + DA + CL) 1028 | 1029 | TKDE 2025, [[PDF]](https://ieeexplore.ieee.org/document/10877773), [[Code]](https://github.com/RSnewbie/RS/tree/master/HCFAA) 1030 | 1031 | 174. **Dynamic Knowledge Selector and Evaluator for Recommendation with Knowledge Graph** (Graph + CL) 1032 | 1033 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2502.15623) 1034 | 1035 | 175. **Next-POI Recommendation via Spatial-Temporal Knowledge Graph Contrastive Learning and Trajectory Prompt** (Graph + CL) 1036 | 1037 | TKDE 2025, [[PDF]](https://ieeexplore.ieee.org/document/10904285) 1038 | 1039 | 176. **Dual-Channel Multiplex Graph Neural Networks for Recommendation** (Graph + CL) 1040 | 1041 | TKDE 2025, [[PDF]](https://ieeexplore.ieee.org/document/10909460), [[Code]](https://github.com/lx970414/TKDE-DCMGNN) 1042 | 1043 | 177. **Uniform Graph Pre-training and Prompting for Transferable Recommendation** (Graph + DA + CL) 1044 | 1045 | TOIS 2025, [[PDF]](https://dl.acm.org/doi/10.1145/3724392), [[Code]](https://github.com/Code2Q/ProRec) 1046 | 1047 | 178. **Diffusion-Augmented Graph Contrastive Learning for Collaborative Filter** (Graph + DA + CL) 1048 | 1049 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2503.16290) 1050 | 1051 | 179. **Diffusion-Augmented Graph Contrastive Learning for Collaborative Filter** (Graph + CL) 1052 | 1053 | SIGIR 2025, [[PDF]](https://arxiv.org/pdf/2504.04443), [[Code]](https://github.com/Zheyu-Chen/WeightedGCL) 1054 | 1055 | 180. **HEK-CL: Hierarchical Enhanced Knowledge-Aware Contrastive Learning for Recommendation** (Graph + DA + CL) 1056 | 1057 | TOIS 2025, [[PDF]](https://dl.acm.org/doi/10.1145/3728463) 1058 | 1059 | 181. **Robust Graph Based Social Recommendation Through Contrastive Multi-View Learning** (Graph + CL) 1060 | 1061 | AAAI 2025, [[PDF]](https://ojs.aaai.org/index.php/AAAI/article/view/33406) 1062 | 1063 | 182. **Sub-Interest-Aware Representation Uniformity for Recommender System** (Graph + CL) 1064 | 1065 | AAAI 2025, [[PDF]](https://ojs.aaai.org/index.php/AAAI/article/view/33345) 1066 | 1067 | 183. **Unveiling Contrastive Learning’s Capability of Neighborhood Aggregation for Collaborative Filtering** (Graph + CL) 1068 | 1069 | SIGIR 2025, [[PDF]](https://arxiv.org/pdf/2504.10113), [[Code]](https://github.com/ZzYUuuu/LightCCF) 1070 | 1071 | 184. **MSCRS: Multi-modal Semantic Graph Prompt Learning Framework for Conversational Recommender Systems** (Graph + CL) 1072 | 1073 | SIGIR 2025, [[PDF]](https://arxiv.org/pdf/2504.10921), [[Code]](https://github.com/BIAOBIAO12138/MSCRS-main) 1074 | 1075 | 185. **Multi-Modal Hypergraph Enhanced LLM Learning for Recommendation** (Graph + Multi-Modal + LLM + CL) 1076 | 1077 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2504.10541), [[Code]](https://github.com/BIAOBIAO12138/MSCRS-main) 1078 | 1079 | 186. **MMHCL: Multi-Modal Hypergraph Contrastive Learning for Recommendation** (Graph + Multi-Modal + CL) 1080 | 1081 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2504.16576), [[Code]](https://github.com/Xu107/MMHCL) 1082 | 1083 | 187. **Hyperbolic Contrastive Learning with Model-augmentation for Knowledge-aware Recommendation** (Graph + DA + CL) 1084 | 1085 | ECML-PKDD 2024, [[PDF]](https://arxiv.org/pdf/2505.08157), [[Code]](https://github.com/sunshy-1/HCMKR) 1086 | 1087 | 188. **Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation** (Graph + DA + CL) 1088 | 1089 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2505.24172) 1090 | 1091 | 1092 | ## Sequential Models with CL 1093 | 1094 | 1. **Uniform Sequence Better: Time Interval Aware Data Augmentation for Sequential Recommendation** (Sequential + CL + DA) 1095 | 1096 | AAAI 2023, [[PDF]](https://arxiv.org/pdf/2212.08262.pdf), [[Code]](https://github.com/KingGugu/TiCoSeRec) 1097 | 1098 | 2. **Contrastive Learning for Sequential Recommendation** (Sequential + CL + DA) 1099 | 1100 | ICDE 2022, [[PDF]](https://arxiv.org/pdf/2010.14395.pdf), [[Code]](https://github.com/RUCAIBox/RecBole-DA/blob/master/recbole/model/sequential_recommender/cl4srec.py) 1101 | 1102 | 3. **Contrastive Self-supervised Sequential Recommendation with Robust Augmentation** (Sequential + CL + DA) 1103 | 1104 | arXiv 2021, [[PDF]](https://arxiv.org/pdf/2108.06479.pdf), [[Code]](https://github.com/YChen1993/CoSeRec) 1105 | 1106 | 4. **Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation** (Sequential + CL + DA) 1107 | 1108 | arXiv 2022, [[PDF]](https://arxiv.org/pdf/2204.10128.pdf) 1109 | 1110 | 5. **S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization** (Sequential + CL + DA) 1111 | 1112 | CIKM 2020, [[PDF]](https://arxiv.org/pdf/2008.07873.pdf), [[Code]](https://github.com/RUCAIBox/CIKM2020-S3Rec) 1113 | 1114 | 6. **Contrastive Curriculum Learning for Sequential User Behavior Modeling via Data Augmentation** (Sequential + CL + DA) 1115 | 1116 | CIKM 2021, [[PDF]](https://www.atailab.cn/seminar2022Spring/pdf/2021_CIKM_Contrastive%20Curriculum%20Learning%20for%20Sequential%20User%20Behavior%20Modeling%20via%20Data%20Augmentation.pdf) , [[Code]](https://github.com/RUCAIBox/Contrastive-Curriculum-Learning) 1117 | 1118 | 7. **Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation** (Sequential + CL + DA) 1119 | 1120 | WSDM 2022, [[PDF]](https://arxiv.org/pdf/2110.05730.pdf), [[Code]](https://github.com/RuihongQiu/DuoRec) 1121 | 1122 | 8. **Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation** (Sequential + CL + DA) 1123 | 1124 | ICDM 2021, [[PDF]](https://arxiv.org/pdf/2109.00368.pdf) 1125 | 1126 | 9. **Contrastive Learning with Bidirectional Transformers for Sequential Recommendation** (Sequential + CL + DA) 1127 | 1128 | CIKM 2022, [[PDF]](https://arxiv.org/pdf/2208.03895.pdf), [[Code]](https://github.com/hw-du/CBiT/tree/master) 1129 | 1130 | 10. **ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation** (Sequential + CL + DA) 1131 | 1132 | CIKM 2022, [[PDF]](https://arxiv.org/pdf/2209.00456.pdf), [[Code]](https://github.com/YuWang-1024/ContrastVAE) 1133 | 1134 | 11. **Temporal Contrastive Pre-Training for Sequential Recommendation** (Sequential + CL + DA) 1135 | 1136 | CIKM 2022, [[PDF]](https://dl.acm.org/doi/10.1145/3511808.3557468), [[Code]](https://github.com/ChangxinTian/TCP-SRec) 1137 | 1138 | 12. **Multi-level Contrastive Learning Framework for Sequential Recommendation** (Graph + Sequential + CL) 1139 | 1140 | CIKM 2022, [[PDF]](https://arxiv.org/pdf/2208.13007.pdf) 1141 | 1142 | 13. **Equivariant Contrastive Learning for Sequential Recommendation** (Sequential + DA + CL) 1143 | 1144 | RecSys 2023, [[PDF]](https://arxiv.org/pdf/2211.05290.pdf), [[Code]](https://github.com/Tokkiu/ECL) 1145 | 1146 | 14. **Explanation Guided Contrastive Learning for Sequential Recommendation** (Sequential + DA + CL) 1147 | 1148 | CIKM 2022, [[PDF]](https://arxiv.org/pdf/2209.01347.pdf), [[Code]](https://github.com/demoleiwang/EC4SRec) 1149 | 1150 | 15. **Intent Contrastive Learning for Sequential Recommendation** (Sequential + DA + CL) 1151 | 1152 | WWW 2022, [[PDF]](https://arxiv.org/pdf/2202.02519.pdf), [[Code]](https://github.com/salesforce/ICLRec) 1153 | 1154 | 16. **Dual Contrastive Network for Sequential Recommendation** (Sequential + CL) 1155 | 1156 | SIGIR 2022, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3477495.3531918) 1157 | 1158 | 17. **Dual Contrastive Network for Sequential Recommendation with User and Item-Centric Perspectives** (Sequential + CL) 1159 | 1160 | arXiv 2022, [[PDF]](https://arxiv.org/pdf/2209.08446.pdf) 1161 | 1162 | 18. **Enhancing Sequential Recommendation with Graph Contrastive Learning** (Sequential + Graph + CL + DA) 1163 | 1164 | IJCAI 2022, [[PDF]](https://arxiv.org/pdf/2205.14837.pdf), [[Code]](https://github.com/sdu-zyx/GCL4SR) 1165 | 1166 | 19. **Disentangling Long and Short-Term Interests for Recommendation** (Sequential + Graph + CL) 1167 | 1168 | WWW 2022, [[PDF]](https://arxiv.org/pdf/2202.13090.pdf), [[Code]](https://github.com/tsinghua-fib-lab/CLSR) 1169 | 1170 | 20. **Hyperbolic Hypergraphs for Sequential Recommendation** (Sequential + Graph + CL + DA) 1171 | 1172 | CIKM 2021, [[PDF]](https://arxiv.org/pdf/2108.08134.pdf), [[Code]](https://github.com/Abigale001/h2seqrec) 1173 | 1174 | 21. **Mutual Wasserstein Discrepancy Minimization for Sequential Recommendation** (Sequential + CL) 1175 | 1176 | WWW 2023, [[PDF]](https://arxiv.org/pdf/2301.12197.pdf), [[Code]](https://github.com/zfan20/MStein) 1177 | 1178 | 22. **Dual-interest Factorization-heads Attention for Sequential Recommendation** (Sequential + CL) 1179 | 1180 | WWW 2023, [[PDF]](https://arxiv.org/pdf/2302.03965.pdf), [[Code]](https://github.com/tsinghua-fib-lab/WWW2023-DFAR) 1181 | 1182 | 23. **GUESR: A Global Unsupervised Data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation** (Sequential + DA + CL) 1183 | 1184 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2303.00243.pdf) 1185 | 1186 | 24. **Self-Supervised Interest Transfer Network via Prototypical Contrastive Learning for Recommendation** (Sequential + CL) 1187 | 1188 | AAAI 2023, [[PDF]](https://arxiv.org/pdf/2302.14438.pdf), [[Code]](https://github.com/fanqieCoffee/SITN-Supplement) 1189 | 1190 | 25. **A Self-Correcting Sequential Recommender** (Sequential + DA + SSL) 1191 | 1192 | WWW 2023, [[PDF]](https://arxiv.org/pdf/2303.02297.pdf), [[Code]](https://github.com/TempSDU/STEAM) 1193 | 1194 | 26. **User Retention-oriented Recommendation with Decision Transformer** (Sequential + CL) 1195 | 1196 | WWW 2023, [[PDF]](https://arxiv.org/pdf/2303.06347.pdf), [[Code]](https://github.com/kesenzhao/DT4Rec) 1197 | 1198 | 27. **Debiased Contrastive Learning for Sequential Recommendation** (Sequential + DA + CL) 1199 | 1200 | WWW 2023, [[PDF]](https://arxiv.org/pdf/2303.11780.pdf), [[Code]](https://github.com/HKUDS/DCRec) 1201 | 1202 | 28. **Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders** (Sequential + CL) 1203 | 1204 | WWW 2023, [[PDF]](https://arxiv.org/pdf/2210.12316.pdf), [[Code]](https://github.com/RUCAIBox/VQ-Rec) 1205 | 1206 | 29. **Sequential Recommendation with Diffusion Models** (Diffsion + Sequential + CL) 1207 | 1208 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2304.04541.pdf) 1209 | 1210 | 30. **Triple Sequence Learning for Cross-domain Recommendation** (Cross-Domain + Sequential + CL) 1211 | 1212 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2304.05027.pdf) 1213 | 1214 | 31. **Contrastive Cross-Domain Sequential Recommendation** (Cross-Domain + Sequential + CL) 1215 | 1216 | CIMK 2022, [[PDF]](https://arxiv.org/pdf/2304.03891.pdf), [[Code]](https://github.com/cjx96/C2DSR) 1217 | 1218 | 32. **Adversarial and Contrastive Variational Autoencoder for Sequential Recommendation** (VAE + Sequential + CL) 1219 | 1220 | WWW 2021, [[PDF]](https://arxiv.org/pdf/2103.10693.pdf), [[Code]](https://github.com/ACVAE/ACVAE-PyTorch) 1221 | 1222 | 33. **Meta-optimized Contrastive Learning for Sequential Recommendation** (Meta + Sequential + CL) 1223 | 1224 | SIGIR 2023, [[PDF]](https://arxiv.org/pdf/2304.07763.pdf), [[Code]](https://github.com/QinHsiu/MCLRec) 1225 | 1226 | 34. **Frequency Enhanced Hybrid Attention Network for Sequential Recommendation** (Sequential + CL) 1227 | 1228 | SIGIR 2023, [[PDF]](https://arxiv.org/pdf/2304.09184.pdf), [[Code]](https://github.com/sudaada/FEARec) 1229 | 1230 | 35. **Self-Supervised Multi-Modal Sequential Recommendation** (Multi-Moda + Sequential + CL) 1231 | 1232 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2304.13277.pdf), [[Code]](https://github.com/kz-song/MMSRec) 1233 | 1234 | 36. **Conditional Denoising Diffusion for Sequential Recommendation** (Diffusion + Sequential + CL) 1235 | 1236 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2304.11433.pdf) 1237 | 1238 | 37. **Ensemble Modeling with Contrastive Knowledge Distillation for Sequential Recommendation** (Diffusion + Sequential + CL) 1239 | 1240 | SIGIR 2023, [[PDF]](https://arxiv.org/pdf/2304.14668.pdf), [[Code]](https://github.com/hw-du/EMKD) 1241 | 1242 | 38. **Multi-view Multi-behavior Contrastive Learning in Recommendation** (Sequential + Graph + CL) 1243 | 1244 | DASFAA 2022, [[PDF]](https://arxiv.org/pdf/2203.10576.pdf), [[Code]](https://github.com/wyqing20/MMCLR) 1245 | 1246 | 39. **Denoising Multi-modal Sequential Recommenders with Contrastive Learning** (Sequential + CL) 1247 | 1248 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2305.01915.pdf) 1249 | 1250 | 40. **Multi-view Multi-behavior Contrastive Learning in Recommendation** (Sequential + Graph + CL) 1251 | 1252 | SIGIR 2022, [[PDF]](https://arxiv.org/pdf/2305.04619.pdf), [[Code]](https://github.com/HKUDS/MAERec) 1253 | 1254 | 41. **Contrastive Enhanced Slide Filter Mixer for Sequential Recommendation** (Sequential + CL) 1255 | 1256 | ICDE 2023, [[PDF]](https://arxiv.org/pdf/2305.04322.pdf), [[Code]](https://github.com/sudaada/SLIME4Rec) 1257 | 1258 | 42. **Contrastive State Augmentations for Reinforcement Learning-Based Recommender Systems** (Sequential + DA + CL) 1259 | 1260 | SIGIR 2023, [[PDF]](https://arxiv.org/pdf/2305.11081.pdf), [[Code]](https://github.com/HN-RS) 1261 | 1262 | 43. **When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation** (Sequential + CL) 1263 | 1264 | SIGIR 2023, [[PDF]](https://arxiv.org/pdf/2305.10822.pdf), [[Code]](https://github.com/Ethan00Si/SESREC-SIGIR-2023) 1265 | 1266 | 44. **Text Is All You Need: Learning Language Representations for Sequential Recommendation** (Sequential + CL) 1267 | 1268 | KDD 2023, [[PDF]](https://arxiv.org/pdf/2305.13731.pdf) 1269 | 1270 | 45. **Sequential Recommendation with Multiple Contrast Signals** (Sequential + CL) 1271 | 1272 | TOIS 2022, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3522673), [[Code]](https://github.com/THUwangcy/ReChorus/tree/TOIS22) 1273 | 1274 | 46. **Robust Reinforcement Learning Objectives for Sequential Recommender Systems** (Sequential + CL) 1275 | 1276 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2305.18820.pdf), [[Code]](https://github.com/melfm/sasrec-ccql) 1277 | 1278 | 47. **AdaptiveRec: Adaptively Construct Pairs for Contrastive Learning in Sequential Recommendation** (Sequential + CL) 1279 | 1280 | PMLR 2023, [[PDF]](https://arxiv.org/pdf/2307.05469.pdf) 1281 | 1282 | 48. **Fisher-Weighted Merge of Contrastive Learning Models in Sequential Recommendation** (Sequential + CL) 1283 | 1284 | PMLR 2023, [[PDF]](https://arxiv.org/pdf/2307.05476.pdf) 1285 | 1286 | 49. **Hierarchical Contrastive Learning with Multiple Augmentation for Sequential Recommendation** (Sequential + DA + CL) 1287 | 1288 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2308.03400.pdf) 1289 | 1290 | 50. **Poisoning Self-supervised Learning Based Sequential Recommendations** (Sequential + Attack + DA + CL) 1291 | 1292 | SIGIR 2023, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3539618.3591751), [[Code]](https://github.com/CongGroup/Poisoning-SSL-based-RS) 1293 | 1294 | 51. **Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation** (Sequential + CL) 1295 | 1296 | SIGIR 2023, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3539618.3591672) 1297 | 1298 | 52. **Leveraging Negative Signals with Self-Attention for Sequential Music Recommendation** (Sequential + CL) 1299 | 1300 | RecSys 2023, [[PDF]](https://arxiv.org/pdf/2309.11623.pdf) 1301 | 1302 | 53. **RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendationn** (Sequential + DA + CL) 1303 | 1304 | CIKM 2023, [[PDF]](https://arxiv.org/pdf/2309.10469.pdf) 1305 | 1306 | 54. **FedDCSR: Federated Cross-domain Sequential Recommendation via Disentangled Representation Learning** (Sequential + DA + CL) 1307 | 1308 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2309.08420.pdf), [[Code]](https://github.com/orion-orion/FedDCSR) 1309 | 1310 | 55. **Unbiased and Robust: External Attention-enhanced Graph Contrastive Learning for Cross-domain Sequential Recommendation** (Sequential + Graph + DA + CL) 1311 | 1312 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2310.04633.pdf), [[Code]](https://github.com/HoupingY/EA-GCL) 1313 | 1314 | 56. **Dual-Scale Interest Extraction Framework with Self-Supervision for Sequential Recommendation** (Sequential + CL) 1315 | 1316 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2310.10025.pdf) 1317 | 1318 | 57. **Intent Contrastive Learning with Cross Subsequences for Sequential Recommendation** (Sequential + DA + CL) 1319 | 1320 | WSDM 2024, [[PDF]](https://arxiv.org/pdf/2310.14318.pdf), [[Code]](https://github.com/QinHsiu/ICSRec) 1321 | 1322 | 58. **Meta-optimized Joint Generative and Contrastive Learning for Sequential Recommendation** (Sequential + DA + CL) 1323 | 1324 | ICDE 2024, [[PDF]](https://arxiv.org/pdf/2310.13925.pdf), [[Code]](https://github.com/YongjingHao/Meta-SGCL) 1325 | 1326 | 59. **Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation** (Sequential + CL) 1327 | 1328 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2310.16566.pdf) 1329 | 1330 | 60. **Periodicity May Be Emanative: Hierarchical Contrastive Learning for Sequential Recommendation** (Sequential + DA + CL) 1331 | 1332 | CIKM 2023, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3583780.3615007), [[Code]](https://github.com/RUCAIBox/RecBole) 1333 | 1334 | 61. **APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation** (Sequential + Graph + CL) 1335 | 1336 | CIKM 2023, [[PDF]](https://arxiv.org/pdf/2311.02816.pdf), [[Code]](https://github.com/Graph-Team/APGL4SR) 1337 | 1338 | 62. **Towards Open-world Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising Approach** (Sequential + CL) 1339 | 1340 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2311.04760.pdf) 1341 | 1342 | 63. **Feature-Level Deeper Self-Attention Network With Contrastive Learning for Sequential Recommendation** (Sequential + CL) 1343 | 1344 | TKDE 2023, [[PDF]](https://ieeexplore.ieee.org/document/10059216) 1345 | 1346 | 64. **Learnable Model Augmentation Contrastive Learning for Sequential Recommendation** (Sequential + CL) 1347 | 1348 | TKDE 2023, [[PDF]](https://ieeexplore.ieee.org/document/10313990) 1349 | 1350 | 65. **Learnable Model Augmentation Contrastive Learning for Sequential Recommendation** (Sequential + DA + CL) 1351 | 1352 | WSDM 2023, [[PDF]](https://www.atailab.cn/seminar2023Spring/pdf/2023_WSDM_Multi-Intention%20Oriented%20Contrastive%20Learning%20for%20Sequential%20Recommendation.pdf), [[Code]](https://github.com/LFM-bot/IOCRec) 1353 | 1354 | 66. **Collaborative Word-based Pre-trained Item Representation for Transferable Recommendation** (Sequential + CL) 1355 | 1356 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2311.10501.pdf), [[Code]](https://github.com/ysh-1998/CoWPiRec) 1357 | 1358 | 67. **Cracking the Code of Negative Transfer:A Cooperative Game Theoretic Approach for Cross-Domain Sequential Recommendation** (Sequential + Cross-Domain + CL) 1359 | 1360 | CIKM 2023, [[PDF]](https://arxiv.org/pdf/2311.13188.pdf) 1361 | 1362 | 68. **Contrastive Multi-View Interest Learning for Cross-Domain Sequential Recommendation** (Sequential + Cross-Domain + CL) 1363 | 1364 | TOIS 2023, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3632402), [[Code]](https://github.com/ZSHKJWBY/CMVCDR) 1365 | 1366 | 69. **E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendation** (Sequential + LLM + CL) 1367 | 1368 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2312.02443.pdf), [[Code]](https://github.com/HestiaSky/E4SRec/) 1369 | 1370 | 70. **TFCSRec: Time-Frequency Consistency Based Contrastive Learning for Sequential Recommendation** (Sequential + CL) 1371 | 1372 | Expert Systems with Applications 2024, [[PDF]](https://www.sciencedirect.com/science/article/pii/S0957417423036229) 1373 | 1374 | 71. **A Relevant and Diverse Retrieval-enhanced Data Augmentation Framework for Sequential Recommendation** (Sequential + DA + CL) 1375 | 1376 | CIMK 2022, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3511808.3557071), [[Code]](https://github.com/RUCAIBox/ReDA) 1377 | 1378 | 72. **high-level preferences as positive examples in contrastive learning for multi-interest sequential recommendation** （Sequential + CL） 1379 | 1380 | Arxiv 2023, [[PDF]](https://assets.researchsquare.com/files/rs-3825823/v1_covered_773bc524-1cf2-454b-88cb-52e5bf0386b0.pdf?c=1704709556) 1381 | 1382 | 73. **Feature-Aware Contrastive Learning with Bidirectional Transformers for Sequential Recommendation** (Sequential + CL) 1383 | 1384 | TKDE 2023, [[PDF]](https://ieeexplore.ieee.org/abstract/document/10375742/) 1385 | 1386 | 74. **End-to-end Learnable Clustering for Intent Learning in Recommendation** (Sequential + CL) 1387 | 1388 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2401.05975.pdf) 1389 | 1390 | 75. **Contrastive Learning with Frequency-Domain Interest Trends for Sequential Recommendation** (Sequential + DA + CL) 1391 | 1392 | RecSys 2023, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3604915.3608790), [[Code]](https://github.com/zhangyichi1Z/CFIT4SRec) 1393 | 1394 | 76. **Sequential Recommendation on Temporal Proximities with Contrastive Learning and Self-Attention** (Sequential + CL) 1395 | 1396 | WWW 2024, [[PDF]](https://arxiv.org/pdf/2402.09784.pdf), [[Code]](https://github.com/TemProxRec) 1397 | 1398 | 77. **End-to-end Graph-Sequential Representation Learning for Accurate Recommendations** (Sequential + Graph + CL) 1399 | 1400 | WWW 2024, [[PDF]](https://arxiv.org/pdf/2403.00895.pdf), [[Code]](https://github.com/NonameUntitled/MRGSRec) 1401 | 1402 | 78. **Multi-Sequence Attentive User Representation Learning for Side-information Integrated Sequential Recommendation** (Sequential + CL) 1403 | 1404 | WSDM 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3616855.3635815), [[Code]](https://github.com/xiaolLIN/MSSR) 1405 | 1406 | 79. **Empowering Sequential Recommendation from Collaborative Signals and Semantic Relatedness** (Sequential + CL) 1407 | 1408 | arxiv 2024, [[PDF]](https://arxiv.org/pdf/2403.07623.pdf) 1409 | 1410 | 80. **Collaborative Sequential Recommendations via Multi-View GNN-Transformers** (Sequential + Graph + CL) 1411 | 1412 | TOIS 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3649436) 1413 | 1414 | 81. **Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential Recommendation** (Sequential + DA + CL) 1415 | 1416 | WWW 2024, [[PDF]](https://arxiv.org/pdf/2403.11136.pdf), [[Code]](https://github.com/AIM-SE/DA4Rec) 1417 | 1418 | 82. **Diversifying Sequential Recommendation with Retrospective and Prospective Transformers** (Sequential + CL) 1419 | 1420 | TOIS 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3653016), [[Code]](https://github.com/chaoyushi/TRIER) 1421 | 1422 | 83. **A Large Language Model Enhanced Sequential Recommender for Joint Video and Comment Recommendation** (Sequential + CL) 1423 | 1424 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2403.13574.pdf), [[Code]](https://github.com/RUCAIBox/LSVCR/) 1425 | 1426 | 84. **Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation** (Sequential + CL) 1427 | 1428 | WWW 2024, [[PDF]](https://arxiv.org/pdf/2403.17603.pdf), [[Code]](https://github.com/huschbsd/END4REC) 1429 | 1430 | 85. **Temporal Graph Contrastive Learning for Sequential Recommendation** (Sequential + Graph + CL) 1431 | 1432 | AAAI 2024, [[PDF]](https://ojs.aaai.org/index.php/AAAI/article/download/28789/29511) 1433 | 1434 | 86. **Sparse Enhanced Network: An Adversarial Generation Method for Robust Augmentation in Sequential Recommendation** (Sequential + DA + CL) 1435 | 1436 | AAAI 2024, [[PDF]](https://ojs.aaai.org/index.php/AAAI/article/download/28669/29299), [[Code]](https://github.com/junyachen/SparseEnNet) 1437 | 1438 | 87. **Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention** (Sequential + CL) 1439 | 1440 | SIGIR 2024, [[PDF]](https://arxiv.org/pdf/2404.03637.pdf), [[Code]](https://anonymous.4open.science/r/DT4IER-5837) 1441 | 1442 | 88. **Beyond the Sequence: Statistics-Driven Pre-training for Stabilizing Sequential Recommendation Model** (Sequential + DA + CL) 1443 | 1444 | RecSys 2023, [[PDF]](https://arxiv.org/pdf/2404.05342.pdf) 1445 | 1446 | 89. **Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential Recommendation** (Sequential + DA + CL) 1447 | 1448 | WWW 2024, [[PDF]](https://arxiv.org/pdf/2404.07219.pdf), [[Code]](https://github.com/xjaw/S4Rec) 1449 | 1450 | 90. **UniSAR: Modeling User Transition Behaviors between Search and Recommendation** (Sequential + CL) 1451 | 1452 | SIGIR 2024, [[PDF]](https://arxiv.org/pdf/2404.09520.pdf), [[Code]](https://github.com/TengShi-RUC/UniSAR) 1453 | 1454 | 91. **Multi-Level Sequence Denoising with Cross-Signal Contrastive Learning for Sequential Recommendation** (Sequential + CL) 1455 | 1456 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2404.13878.pdf), [[Code]](https://github.com/lalunex/MSDCCL/tree/main) 1457 | 1458 | 92. **Contrastive Learning Method for Sequential Recommendation based on Multi-Intention Disentanglement** (Sequential + CL) 1459 | 1460 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2404.18214) 1461 | 1462 | 93. **CALRec: Contrastive Alignment of Generative LLMs For Sequential Recommendation** (Sequential + LLM + CL) 1463 | 1464 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2405.02429) 1465 | 1466 | 94. **ID-centric Pre-training for Recommendation** (Sequential + CL) 1467 | 1468 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2405.03562) 1469 | 1470 | 95. **Context Matters: Enhancing Sequential Recommendation with Context-aware Diffusion-based Contrastive Learning** (Sequential + DA + CL) 1471 | 1472 | CIKM 2024, [[PDF]](https://arxiv.org/pdf/2405.09369), [[Code]](https://github.com/ziqiangcui/CaDiRec) 1473 | 1474 | 96. **Soft Contrastive Sequential Recommendation** (Sequential + DA + CL) 1475 | 1476 | TOIS 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3665325) 1477 | 1478 | 97. **Modeling User Fatigue for Sequential Recommendation** (Sequential + DA + CL) 1479 | 1480 | SIGIR 2024, [[PDF]](https://arxiv.org/pdf/2405.11764), [[Code]](https://github.com/tsinghua-fib-lab/SIGIR24-FRec) 1481 | 1482 | 98. **Aligned Side Information Fusion Method for Sequential Recommendation** (Sequential + CL) 1483 | 1484 | WWW 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3589335.3648308) 1485 | 1486 | 99. **Learning Partially Aligned Item Representation for Cross-Domain Sequential Recommendation** (Sequential + CL) 1487 | 1488 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2405.12473), [[Code]](https://anonymous.4open.science/r/KDD2024-58E8/) 1489 | 1490 | 100. **SelfGNN: Self-Supervised Graph Neural Networks for Sequential Recommendation** (Sequential + Graph + CL) 1491 | 1492 | SIGIR 2024, [[PDF]](https://arxiv.org/pdf/2405.20878), [[Code]](https://github.com/HKUDS/SelfGNN) 1493 | 1494 | 101. **Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential Recommendation** (Sequential + CL) 1495 | 1496 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2406.03085), [[Code]](https://github.com/TingJShen/URLLM) 1497 | 1498 | 102. **PTF-FSR: A Parameter Transmission-Free Federated Sequential Recommender System** (Sequential + CL) 1499 | 1500 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2406.05387) 1501 | 1502 | 103. **Pacer and Runner: Cooperative Learning Framework between Single- and Cross-Domain Sequential Recommendation** (Sequential + CL) 1503 | 1504 | SIGIR 2024, [[PDF]](https://arxiv.org/pdf/2407.11245), [[Code]](https://github.com/cpark88/SyNCRec) 1505 | 1506 | 104. **Scaling Sequential Recommendation Models with Transformers** (Sequential + CL) 1507 | 1508 | SIGIR 2024, [[PDF]](https://arxiv.org/pdf/2412.07585), [[Code]](https://github.com/mercadolibre/srt) 1509 | 1510 | 105. **CMCLRec: Cross-modal Contrastive Learning for User Cold-start Sequential Recommendation** (Sequential + CL) 1511 | 1512 | SIGIR 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3626772.3657839) 1513 | 1514 | 106. **Multimodal Pre-training for Sequential Recommendation via Contrastive Learning** (Sequential + CL) 1515 | 1516 | TORS 2024, [[PDF]](https://arxiv.org/pdf/2303.11879.pdf) 1517 | 1518 | 107. **Beyond Inter-Item Relations: Dynamic Adaptive Mixture-of-Experts for LLM-Based Sequential Recommendation** (Sequential + LLM + CL) 1519 | 1520 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2408.07427) 1521 | 1522 | 108. **Contrastive Learning on Medical Intents for Sequential Prescription Recommendation** (Sequential + CL) 1523 | 1524 | CIKM 2024, [[PDF]](https://arxiv.org/pdf/2408.10259), [[Code]](https://github.com/aryahm1375/ARCI) 1525 | 1526 | 109. **Disentangled Multi-interest Representation Learning for Sequential Recommendation** (Sequential + CL) 1527 | 1528 | KDD 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3637528.3671800) 1529 | 1530 | 110. **Multi-intent Aware Contrastive Learning for Sequential Recommendation** (Sequential + CL) 1531 | 1532 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2409.08733) 1533 | 1534 | 111. **Large Language Model Empowered Embedding Generator for Sequential Recommendation** (Sequential + LLM + CL) 1535 | 1536 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2409.08733), [[Code]](https://github.com/liuqidong07/LLMEmb) 1537 | 1538 | 112. **FELLAS: Enhancing Federated Sequential Recommendation with LLM as External Services** (Sequential + LLM + CL) 1539 | 1540 | TOIS 2024, [[PDF]](https://arxiv.org/pdf/2410.04927) 1541 | 1542 | 113. **Sequential Recommendation with Collaborative Explanation via Mutual Information Maximization** (Sequential + CL) 1543 | 1544 | SIGIR 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3626772.3657770), [[Code]](https://github.com/yiyualt/SCEMIM) 1545 | 1546 | 114. **Intent-Enhanced Data Augmentation for Sequential Recommendation** (Sequential + DA + CL) 1547 | 1548 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2410.08583), [[Code]](https://github.com/yiyualt/SCEMIM) 1549 | 1550 | 115. **Relative Contrastive Learning for Sequential Recommendation with Similarity-based Positive Sample Selection** (Sequential + CL) 1551 | 1552 | CIKM 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3627673.3679681), [[Code]](https://github.com/Cloudcatcher888/RCL) 1553 | 1554 | 116. **Context Matters: Enhancing Sequential Recommendation with Context-aware Diffusion-based Contrastive Learning** (Sequential + DA + CL) 1555 | 1556 | CIKM 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3627673.3679655), [[Code]](https://github.com/ziqiangcui/CaDiRec) 1557 | 1558 | 117. **Momentum Contrastive Bidirectional Encoding with Self-Distillation for Sequential Recommendation** (Sequential + CL) 1559 | 1560 | CIKM 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3627673.3679965) 1561 | 1562 | 118. **AuriSRec: Adversarial User Intention Learning in Sequential Recommendation** (Sequential + CL) 1563 | 1564 | EMNLP 2024 (Findings), [[PDF]](https://aclanthology.org/2024.findings-emnlp.735.pdf) 1565 | 1566 | 119. **AuriSRec: Adversarial User Intention Learning in Sequential Recommendation** (Sequential + LLM + CL) 1567 | 1568 | EMNLP 2024 (Findings), [[PDF]](https://aclanthology.org/2024.findings-emnlp.423.pdf) 1569 | 1570 | 120. **LLM-assisted Explicit and Implicit Multi-interest Learning Framework for Sequential Recommendation** (Sequential + LLM + CL) 1571 | 1572 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2411.09410) 1573 | 1574 | 121. **Temporal Linear Item-Item Model for Sequential Recommendation** (Sequential + DA + CL) 1575 | 1576 | WSDM 2025, [[PDF]](https://arxiv.org/pdf/2412.07382), [[Code]](https://github.com/psm1206/TALE) 1577 | 1578 | 122. **PTF-FSR: A Parameter Transmission-Free Federated Sequential Recommender System** (Sequential + CL) 1579 | 1580 | TOIS 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3708344), [[Code]](https://github.com/hi-weiyuan/PTF-FSR) 1581 | 1582 | 123. **Future Sight and Tough Fights: Revolutionizing Sequential Recommendation with FENRec** (Sequential + DA + CL) 1583 | 1584 | AAAI 2025, [[PDF]](https://arxiv.org/pdf/2412.11589) 1585 | 1586 | 124. **Lightweight yet Fine-grained: A Graph Capsule Convolutional Network with Subspace Alignment for Shared-account Sequential Recommendation** (Graph + Sequential + CL) 1587 | 1588 | AAAI 2025, [[PDF]](https://arxiv.org/pdf/2412.13408), [[Code]](https://github.com/ZZY-GraphMiningLab/LightGC2N) 1589 | 1590 | 125. **Molar: Multimodal LLMs with Collaborative Filtering Alignment for Enhanced Sequential Recommendation** (Sequential + LLM + CL) 1591 | 1592 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2412.18176), [[Code]](https://anonymous.4open.science/r/Molar-8B06/) 1593 | 1594 | 126. **Intent-Interest Disentanglement and Item-Aware Intent Contrastive Learning for Sequential Recommendation** (Sequential + DA + CL) 1595 | 1596 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2501.07096) 1597 | 1598 | 127. **FedCSR: A Federated Framework for Multi-Platform Cross-Domain Sequential Recommendation with Dual Contrastive Learning** (Sequential + DA + CL) 1599 | 1600 | COLING 2025, [[PDF]](https://aclanthology.org/2025.coling-main.581/), [[Code]](https://github.com/zdy769243418/FedCSR-v1) 1601 | 1602 | 128. **Intent Contrastive Learning Based on Multi-view Augmentation for Sequential Recommendation** (Sequential + DA + CL) 1603 | 1604 | COLING 2025, [[PDF]](https://aclanthology.org/2025.coling-main.222/) 1605 | 1606 | 129. **Knowledge-Guided Semantically Consistent Contrastive Learning for Sequential Recommendation** (Sequential + DA + CL) 1607 | 1608 | NN 2025, [[PDF]](https://www.sciencedirect.com/science/article/pii/S089360802500070X), [[Code]](https://github.com/LFM-bot/KGSCL) 1609 | 1610 | 130. **Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential Recommendation** (Sequential + Multi-Modal + CL) 1611 | 1612 | WWW 2025, [[PDF]](https://arxiv.org/abs/2501.14269), [[Code]](https://github.com/SStarCCat/HM4SR) 1613 | 1614 | 131. **LLMCDSR: Enhancing Cross-Domain Sequential Recommendation with Large Language Models** (Sequential + LLM + CL) 1615 | 1616 | TOIS 2025, [[PDF]](https://dl.acm.org/doi/10.1145/3715099) 1617 | 1618 | 132. **Distinguished Quantized Guidance for Diffusion-based Sequence Recommendation** (Sequential + CL) 1619 | 1620 | WWW 2025, [[PDF]](https://arxiv.org/pdf/2501.17670) 1621 | 1622 | 133. **TagRec: Temporal-Aware Graph Contrastive Learning with Theoretical Augmentation for Sequential Recommendation** (Graph + DA + Sequential + CL) 1623 | 1624 | TKDE 2025, [[PDF]](https://ieeexplore.ieee.org/document/10872817) 1625 | 1626 | 134. **Review-Enhanced Universal Sequence Representation Learning for Recommender Systems** (Sequential + CL) 1627 | 1628 | TOIS 2025, [[PDF]](https://dl.acm.org/doi/10.1145/3717832) 1629 | 1630 | 135. **ARTS: A General and Efficient Multi-Task Self-Prompt Framework for Explainable Sequential Recommendation** (Sequential + DA + CL) 1631 | 1632 | TOIS 2025, [[PDF]](https://dl.acm.org/doi/10.1145/3717833) 1633 | 1634 | 136. **Semantic Retrieval Augmented Contrastive Learning for Sequential Recommendation** (Sequential + CL) 1635 | 1636 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2503.04162) 1637 | 1638 | 137. **Dual-Channel Multiplex Graph Neural Networks for Recommendation** (Sequential + Graph + DA + CL) 1639 | 1640 | TKDD 2025, [[PDF]](https://dl.acm.org/doi/10.1145/3722561) 1641 | 1642 | 138. **Federated Mixture-of-Expert for Non-Overlapped Cross-Domain Sequential Recommendation** (Sequential + Cross-Domain + CL) 1643 | 1644 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2503.13254) 1645 | 1646 | 139. **Self-supervised Graph Neural Sequential Recommendation with Disentangling Long and Short-Term Interest** (Sequential + Graph + CL) 1647 | 1648 | TORS 2025, [[PDF]](https://dl.acm.org/doi/10.1145/3723173), [[Code]](https://github.com/jiubaoyibao/LS4SRec) 1649 | 1650 | 140. **Learnable Sequence Augmenter for Triplet Contrastive Learning in Sequential Recommendation** (Sequential + DA + CL) 1651 | 1652 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2503.20232) 1653 | 1654 | 141. **Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendation** (Sequential + LLM + CL) 1655 | 1656 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2503.22675), [[Code]](https://github.com/TangJiakai/ReaRec) 1657 | 1658 | 142. **Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendation** (Sequential + DA + CL) 1659 | 1660 | TOIS 2025, [[PDF]](https://dl.acm.org/doi/10.1145/3727645), [[Code]](https://github.com/WHUIR/Horae) 1661 | 1662 | 143. **Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendation** (Sequential + CL) 1663 | 1664 | SIGIR 2025, [[PDF]](https://arxiv.org/pdf/2504.04405) 1665 | 1666 | 144. **Diversity-aware Dual-promotion Poisoning Attack on Sequential Recommendation** (Sequential + CL) 1667 | 1668 | SIGIR 2025, [[PDF]](https://arxiv.org/pdf/2504.06586) 1669 | 1670 | 145. **BBQRec: Behavior-Bind Quantization for Multi-Modal Sequential Recommendation** (Sequential + Multi-Modal + CL) 1671 | 1672 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2504.06636) 1673 | 1674 | 146. **Intent Oriented Contrastive Learning for Sequential Recommendation** (Sequential + DA + CL) 1675 | 1676 | AAAI 2025, [[PDF]](https://ojs.aaai.org/index.php/AAAI/article/view/33390) 1677 | 1678 | 147. **JEPA4Rec: Learning Effective Language Representations for Sequential Recommendation via Joint Embedding Predictive Architecture** (Sequential + DA + CL) 1679 | 1680 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2504.10512) 1681 | 1682 | 148. **Intent-aware Diffusion with Contrastive Learning for Sequential Recommendation** (Sequential + DA + CL) 1683 | 1684 | SIGIR 2025, [[PDF]](https://arxiv.org/pdf/2504.16077), [[Code]](https://github.com/qyp9909/InDiRec) 1685 | 1686 | 149. **Bridge the Domains: Large Language Models Enhanced Cross-domain Sequential Recommendation** (Cross-domain + LLM + Sequential + CL) 1687 | 1688 | SIGIR 2025, [[PDF]](https://arxiv.org/pdf/2504.18383), [[Code]](https://github.com/Applied-Machine-Learning-Lab/LLM4CDSR-pytorch) 1689 | 1690 | 150. **ID-Centric Pre-Training for Recommendation** (Sequential + CL) 1691 | 1692 | TOIS 2025, [[PDF]](https://dl.acm.org/doi/10.1145/3735128) 1693 | 1694 | 151. **DIFF: Dual Side-Information Filtering and Fusion for Sequential Recommendation** (Sequential + CL) 1695 | 1696 | SIGIR 2025, [[PDF]](https://arxiv.org/pdf/2505.13974), [[Code]](https://github.com/HyeYoung1218/DIFF) 1697 | 1698 | 1699 | ## Other Tasks with CL 1700 | 1701 | 1. **CL4CTR: A Contrastive Learning Framework for CTR Prediction** (CTR + CL) 1702 | 1703 | WSDM 2023, [[PDF]](https://arxiv.org/pdf/2212.00522.pdf), [[Code]](https://github.com/cl4ctr/cl4ctr) 1704 | 1705 | 2. **CCL4Rec: Contrast over Contrastive Learning for Micro-video Recommendation** (Micro Video + CL) 1706 | 1707 | arXiv 2022, [[PDF]](https://arxiv.org/pdf/2208.08024.pdf) 1708 | 1709 | 3. **Re4: Learning to Re-contrast, Re-attend, Re-construct for Multi-interest Recommendation** (Multi Interest + CL) 1710 | 1711 | WWW 2022, [[PDF]](https://arxiv.org/pdf/2208.08011.pdf), [[Code]](https://github.com/DeerSheep0314/Re4-Learning-to-Re-contrast-Re-attend-Re-construct-for-Multi-interest-Recommendation) 1712 | 1713 | 4. **Interventional Recommendation with Contrastive Counterfactual Learning for Better Understanding User Preferences** (Counterfactual + DA + CL) 1714 | 1715 | arXiv 2022, [[PDF]](https://arxiv.org/pdf/2208.06746.pdf) 1716 | 1717 | 5. **Multi-granularity Item-based Contrastive Recommendation** (Industry + CL) 1718 | 1719 | arXiv 2022, [[PDF]](https://arxiv.org/pdf/2207.01387.pdf) 1720 | 1721 | 6. **Improving Micro-video Recommendation via Contrastive Multiple Interests** (Micro Video + CL) 1722 | 1723 | SIGIR 2022, [[PDF]](https://arxiv.org/pdf/2205.09593.pdf) 1724 | 1725 | 7. **Exploiting Negative Preference in Content-based Music Recommendation with Contrastive Learning** (Music Rec + CL) 1726 | 1727 | RecSys 2022, [[PDF]](https://arxiv.org/pdf/2103.09410.pdf), [[Code]](https://github.com/Spijkervet/CLMR) 1728 | 1729 | 8. **Self-supervised Learning for Large-scale Item Recommendations** (Industry + CL + DA) 1730 | 1731 | CIKM 2021, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3459637.3481952) 1732 | 1733 | 9. **CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation** (Bundle Rec + CL) 1734 | 1735 | KDD 2023, [[PDF]](https://arxiv.org/pdf/2206.00242.pdf), [[Code]](https://github.com/mysbupt/CrossCBR) 1736 | 1737 | 10. **Contrastive Learning for Cold-start Recommendation** (Cold Start + CL) 1738 | 1739 | ACM MM (ACM International Conference on Multimedia) 2021, [[PDF]](https://arxiv.org/pdf/2107.05315v1.pdf), [[Code]](https://github.com/weiyinwei/CLCRec) 1740 | 1741 | 11. **Socially-aware Dual Contrastive Learning for Cold-Start Recommendation** (Cold Start + CL) 1742 | 1743 | SIGIR 2022, [[PDF]](https://dl.acm.org/doi/10.1145/3477495.3531780) 1744 | 1745 | 12. **Multi-modal Graph Contrastive Learning for Micro-video Recommendation** (Cold Start + Graph + CL) 1746 | 1747 | SIGIR 2022, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3477495.3531780) 1748 | 1749 | 13. **Self-supervised Learning for Multimedia Recommendation** (Multi Media + Graph + DA + CL) 1750 | 1751 | TMM (IEEE Transactions on Multimedia) 2022, [[PDF]](https://arxiv.org/pdf/2107.05315v1.pdf), [[Code]](https://github.com/zltao/SLMRec/) 1752 | 1753 | 14. **SelfCF: A Simple Framework for Self-supervised Collaborative Filtering** (CF + Graph + DA + CL) 1754 | 1755 | ACM MM (ACM International Conference on Multimedia) 2021, [[PDF]](https://arxiv.org/pdf/2107.03019.pdf), [[Code]](https://github.com/enoche/SelfCF) 1756 | 1757 | 15. **Trading Hard Negatives and True Negatives:A Debiased Contrastive Collaborative Filtering Approach** (CF + CL) 1758 | 1759 | IJCAI 2022, [[PDF]](https://arxiv.org/pdf/2204.11752.pdf) 1760 | 1761 | 16. **The World is Binary: Contrastive Learning for Denoising Next Basket Recommendation** (Next Basket + CL) 1762 | 1763 | SIGIR 2021, [[PDF]](https://dl.acm.org/doi/10.1145/3404835.3462836) 1764 | 1765 | 17. **MIC: Model-agnostic Integrated Cross-channel Recommender** (Industry + CL + DA) 1766 | 1767 | CIKM 2022, [[PDF]](https://arxiv.org/pdf/2110.11570.pdf) 1768 | 1769 | 18. **A Contrastive Sharing Model for Multi-Task Recommendation** (Multi Task + CL) 1770 | 1771 | WWW 2022, [[PDF]](https://dl.acm.org/doi/10.1145/3485447.3512043) 1772 | 1773 | 19. **C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System** (Conversational Rec + CL) 1774 | 1775 | WSDM 2022, [[PDF]](https://arxiv.org/pdf/2201.02732.pdf), [[Code]](https://github.com/RUCAIBox/WSDM2022-C2CRS) 1776 | 1777 | 20. **Contrastive Cross-domain Recommendation in Matching** (Cross-domain Rec + DA + CL) 1778 | 1779 | KDD 2022, [[PDF]](https://arxiv.org/pdf/2112.00999.pdf), [[Code]](https://github.com/lqfarmer/CCDR) 1780 | 1781 | 21. **Contrastive Cross-Domain Sequential Recommendation** (Cross-Domain + Sequential + CL) 1782 | 1783 | CIKM 2022, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3511808.3557262), [[Code]](https://github.com/cjx96/C2DSR) 1784 | 1785 | 22. **Prototypical Contrastive Learning and Adaptive Interest Selection for Candidate Generation in Recommendations** (Industry + CL + DA) 1786 | 1787 | CIKM 2022, [[PDF]](https://arxiv.org/pdf/2211.12893.pdf), [[Code]](https://github.com/cjx96/C2DSR) 1788 | 1789 | 23. **Spatio-Temporal Contrastive Learning Enhanced GNNs for Session-based Recommendation** (GNN + CL) 1790 | 1791 | TOIS 2022, under review, [[PDF]](https://arxiv.org/pdf/2209.11461v2.pdf) 1792 | 1793 | 24. **Disentangled Causal Embedding With Contrastive Learning For Recommender System** (Causal + CL) 1794 | 1795 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2302.03248.pdf), [[Code]](https://github.com/somestudies/DCCL) 1796 | 1797 | 25. **Contrastive Collaborative Filtering for Cold-Start Item Recommendation** (CF + Cold Start + CL) 1798 | 1799 | WWW 2023, [[PDF]](https://arxiv.org/pdf/2302.02151.pdf), [[Code]](https://github.com/zzhin/CCFCRec) 1800 | 1801 | 26. **Cross-domain recommendation via user interest alignment** (Cross-Domain Rec + CL) 1802 | 1803 | WWW 2023, [[PDF]](https://arxiv.org/pdf/2301.11467.pdf), [[Code]](https://github/anonymous/COAST) 1804 | 1805 | 27. **Multi-Modal Self-Supervised Learning for Recommendation** (Multi-Modal Rec + CL) 1806 | 1807 | WWW 2023, [[PDF]](https://arxiv.org/pdf/2302.10632.pdf), [[Code]](https://github.com/HKUDS/MMSSL) 1808 | 1809 | 28. **Efficient On-Device Session-Based Recommendation** (Session + DA + CL) 1810 | 1811 | TOIS 2023, [[PDF]](https://arxiv.org/pdf/2209.13422.pdf), [[Code]](https://github.com/xiaxin1998/EODRec) 1812 | 1813 | 29. **On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation** (Session + DA + CL) 1814 | 1815 | SIGIR 2022, [[PDF]](https://arxiv.org/pdf/2204.11091.pdf), [[Code]](https://github.com/xiaxin1998/OD-Rec) 1816 | 1817 | 30. **Modality Matches Modality: Pretraining Modality-Disentangled Item Representations for Recommendation** (Multi-Modal Rec + CL) 1818 | 1819 | WWW 2022, [[PDF]](https://web.archive.org/web/20220428140054id_/https://dl.acm.org/doi/pdf/10.1145/3485447.3512079), [[Code]](https://github.com/hantengyue/PAMD) 1820 | 1821 | 31. **End-to-End Personalized Next Location Recommendation via Contrastive User Preference Modeling** (POI Rec + CL) 1822 | 1823 | arXiv 2023, [[PDF]](https://arxiv.org/abs/2303.12507) 1824 | 1825 | 32. **Bootstrap Latent Representations for Multi-modal Recommendation** (Multi-Modal Rec + CL) 1826 | 1827 | WWW 2023, [[PDF]](https://arxiv.org/abs/2207.05969), [[Code]](https://github.com/enoche/BM3) 1828 | 1829 | 33. **Simplifying Content-Based Neural News Recommendation: On User Modeling and Training Objectives** (News Rec + CL) 1830 | 1831 | SIGIR 2023, [[PDF]](https://arxiv.org/abs/2304.03112), [[Code]](https://github.com/andreeaiana/simplifying_nnr) 1832 | 1833 | 34. **Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search** (CTR + CL) 1834 | 1835 | CIKM 2022, [[PDF]](https://arxiv.org/abs/2304.02089) 1836 | 1837 | 35. **Cross-Domain Recommendation to Cold-Start Users via Variational Information Bottleneck** (Cross-Domain + CL) 1838 | 1839 | ICDE 2022, [[PDF]](https://arxiv.org/abs/2304.02089), [[Code]](https://github.com/cjx96/CDRIB) 1840 | 1841 | 36. **DisenCDR: Learning Disentangled Representations for Cross-Domain Recommendation** (Cross-Domain + CL) 1842 | 1843 | SIGIR 2022, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3477495.3531967), [[Code]](https://github.com/cjx96/DisenCDR) 1844 | 1845 | 37. **Towards Universal Cross-Domain Recommendation** (Cross-domain + CL) 1846 | 1847 | WSDM 2023, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3539597.3570366), [[Code]](https://github.com/cjx96/UniCDR) 1848 | 1849 | 38. **Dual-Ganularity Contrastive Learning for Session-based Recommendation** (Session + CL) 1850 | 1851 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2304.08873.pdf) 1852 | 1853 | 39. **Discreetly Exploiting Inter-session Information for Session-based Recommendation** (Session Rec + CL) 1854 | 1855 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2304.08894.pdf) 1856 | 1857 | 40. **PerCoNet: News Recommendation with Explicit Persona and Contrastive Learning** (News Rec + CL) 1858 | 1859 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2304.07923.pdf) 1860 | 1861 | 41. **Hierarchical and Contrastive Representation Learning for Knowledge-aware Recommendation** (Knowledge Aware + CL) 1862 | 1863 | ICME 2023, [[PDF]](https://arxiv.org/pdf/2304.07506.pdf) 1864 | 1865 | 42. **Attention-guided Multi-step Fusion: A Hierarchical Fusion Network for Multimodal Recommendation** (Multi-Modal + CL) 1866 | 1867 | SIGIR 2023, [[PDF]](https://arxiv.org/pdf/2304.11979.pdf) 1868 | 1869 | 43. **PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Training** (Fed Rec + CL) 1870 | 1871 | arXiv 2023, [[PDF]](https://arxiv.org/abs/2305.06622) 1872 | 1873 | 44. **UniTRec: A Unified Text-to-Text Transformer and Joint Contrastive Learning Framework for Text-based Recommendation** (Text Based Rec + CL) 1874 | 1875 | ACL 2023, [[PDF]](https://arxiv.org/pdf/2305.15756.pdf), [[Code]](https://github.com/Veason-silverbullet/UniTRec) 1876 | 1877 | 45. **Multi-behavior Self-supervised Learning for Recommendation** (Multi-Behavior + CL) 1878 | 1879 | SIGIR 2023, [[PDF]](https://arxiv.org/pdf/2305.18238v1.pdf), [[Code]](https://github.com/Scofield666/MBSSL) 1880 | 1881 | 46. **Learning Similarity among Users for Personalized Session-Based Recommendation from hierarchical structure of User-Session-Item** (Session Rec + CL) 1882 | 1883 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2306.03040.pdf) 1884 | 1885 | 47. **Securing Visually-Aware Recommender Systems: An Adversarial Image Reconstruction and Detection Framework** (Visually Rec + CL) 1886 | 1887 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2306.07992.pdf) 1888 | 1889 | 48. **Disentangled Contrastive Learning for Cross-Domain Recommendation** (Cross-Domain + CL) 1890 | 1891 | DASFAA 2023, [[PDF]](https://link.springer.com/chapter/10.1007/978-3-031-30672-3_11) 1892 | 1893 | 49. **ContentCTR: Frame-level Live Streaming Click-Through Rate Prediction with Multimodal Transformer** (CTR + CL) 1894 | 1895 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2306.14392.pdf) 1896 | 1897 | 50. **Contrastive Learning for Conversion Rate Prediction** (CVR + CL) 1898 | 1899 | SIGIR 2023, [[PDF]](https://arxiv.org/pdf/2307.05974.pdf), [[Code]](https://github.com/DongRuiHust/CL4CVR) 1900 | 1901 | 51. **Language-Enhanced Session-Based Recommendation with Decoupled Contrastive Learning** (Session Rec + CL) 1902 | 1903 | KDD 2023, [[PDF]](https://arxiv.org/pdf/2307.10650.pdf), [[Code]](https://github.com/gaozhanfire//KDDCup2023) 1904 | 1905 | 52. **Multi-view Hypergraph Contrastive Policy Learning for Conversational Recommendation** (Conversational Rec + CL) 1906 | 1907 | SIGIR 2023, [[PDF]](https://arxiv.org/pdf/2307.14024.pdf), [[Code]](https://github.com/Snnzhao/MH) 1908 | 1909 | 53. **Gaussian Graph with Prototypical Contrastive Learning in E-Commerce Bundle Recommendation** (Bundle Rec + CL) 1910 | 1911 | arXiv 2023, [[PDF]](https://arxiv.org/abs/2307.13468), [[Code]](https://github.com/Snnzhao/MH) 1912 | 1913 | 54. **Contrastive Learning for Conversion Rate Prediction** (CVR + CL) 1914 | 1915 | SIGIR 2023, [[PDF]](https://arxiv.org/pdf/2307.05974.pdf), [[Code]](https://github.com/DongRuiHust/CL4CVR) 1916 | 1917 | 55. **Review-based Multi-intention Contrastive Learning for Recommendation** (Review + CL) 1918 | 1919 | SIGIR 2023, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3539618.3592053) 1920 | 1921 | 56. **CSPM: A Contrastive Spatiotemporal Preference Model for CTR Prediction in On-Demand Food Delivery Services** (CTR + CL) 1922 | 1923 | CIKM 2023, [[PDF]](https://arxiv.org/pdf/2308.08446.pdf) 1924 | 1925 | 57. **MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation** (Multi-Modal + CL) 1926 | 1927 | MM 2023, [[PDF]](https://arxiv.org/pdf/2308.11175.pdf), [[Code]](https://github.com/gimpong/MM23-MISSRec) 1928 | 1929 | 58. **MUSE: Music Recommender System with Shuffle Play Recommendation Enhancement** (Music Rec + DA + CL) 1930 | 1931 | CIKM 2023, [[PDF]](https://arxiv.org/pdf/2308.09649.pdf), [[Code]](https://github.com/yunhak0/MUSE) 1932 | 1933 | 59. **Multi-aspect Graph Contrastive Learning for Review-enhanced Recommendation** (Review + CL) 1934 | 1935 | TOIS 2023, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3618106) 1936 | 1937 | 60. **Interpretable User Retention Modeling in Recommendation** (User Modelling + CL) 1938 | 1939 | RecSys 2023, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3604915.3608818), [[Code]](https://github.com/dinry/IURO) 1940 | 1941 | 61. **Beyond Co-occurrence: Multi-modal Session-based Recommendation** (Session Rec + CL) 1942 | 1943 | TKDE 2023, [[PDF]](https://arxiv.org/pdf/2309.17037.pdf), [[Code]](https://github.com/Zhang-xiaokun/MMSBR) 1944 | 1945 | 62. **Representation Learning with Large Language Models for Recommendation** (LLM + CL) 1946 | 1947 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2310.15950.pdf), [[Code]](https://github.com/HKUDS/RLMRec) 1948 | 1949 | 63. **Universal Multi-modal Multi-domain Pre-trained Recommendation** (Pre-trained + CL) 1950 | 1951 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2311.01831.pdf) 1952 | 1953 | 64. **Towards Hierarchical Intent Disentanglement for Bundle Recommendation** (Bundle Rec + CL) 1954 | 1955 | TKDE 2023, [[PDF]](https://ieeexplore.ieee.org/abstract/document/10304376) 1956 | 1957 | 65. **ControlRec: Bridging the Semantic Gap between Language Model and Personalized Recommendation** (LLM + CL) 1958 | 1959 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2311.16441.pdf) 1960 | 1961 | 66. **Enhancing Item-level Bundle Representation for Bundle Recommendation** (Bundle Rec + CL) 1962 | 1963 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2311.16892.pdf), [[Code]](https://github.com/answermycode/EBRec) 1964 | 1965 | 67. **MultiCBR: Multi-view Contrastive Learning for Bundle Recommendation** (Bundle Rec + CL) 1966 | 1967 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2311.16751.pdf), [[Code]](https://github.com/HappyPointer/MultiCBR) 1968 | 1969 | 68. **Poisoning Attacks Against Contrastive Recommender Systems** (Attack Rec + CL) 1970 | 1971 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2311.18244.pdf) 1972 | 1973 | 69. **PEACE: Prototype lEarning Augmented transferable framework for Cross-domain rEcommendation** (Cross-domain + CL) 1974 | 1975 | WSDM 2024, [[PDF]](https://arxiv.org/pdf/2312.01916.pdf) 1976 | 1977 | 70. **(Debiased) Contrastive Learning Loss for Recommendation (Technical Report)** (Analysis + CL) 1978 | 1979 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2312.08517.pdf) 1980 | 1981 | 71. **Revisiting Recommendation Loss Functions through Contrastive Learning (Technical Report)** (Analysis + CL) 1982 | 1983 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2312.08520.pdf) 1984 | 1985 | 72. **Hierarchical Alignment With Polar Contrastive Learning for Next-Basket Recommendation** (Next Basket + CL) 1986 | 1987 | TKDE 2023, [[PDF]](https://ieeexplore.ieee.org/document/10144403) 1988 | 1989 | 73. **CETN: Contrast-enhanced Through Network for CTR Prediction** (CTR + CL) 1990 | 1991 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2312.09715.pdf) 1992 | 1993 | 74. **Multi-Modality is All You Need for Transferable Recommender Systems** (Transferable Rec + CL) 1994 | 1995 | ICDE 2024, [[PDF]](https://arxiv.org/pdf/2312.09602.pdf), [[Code]](https://github.com/ICDE24/PMMRec) 1996 | 1997 | 75. **RIGHT: Retrieval-augmented Generation for Mainstream Hashtag Recommendation** (Hashtag Rec + CL) 1998 | 1999 | ECIR 2024, [[PDF]](https://arxiv.org/pdf/2312.10466.pdf), [[Code]](https://github.com/ict-bigdatalab/RIGHT) 2000 | 2001 | 76. **AT4CTR: Auxiliary Match Tasks for Enhancing Click-Through Rate Prediction** (CTR + CL) 2002 | 2003 | AAAI 2024, [[PDF]](https://arxiv.org/pdf/2312.06683.pdf) 2004 | 2005 | 77. **Attribute-driven Disentangled Representation Learning for Multimodal Recommendation** (Multi-Modal + CL) 2006 | 2007 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2312.14433.pdf) 2008 | 2009 | 78. **TopicVAE: Topic-aware Disentanglement Representation Learning for Enhanced Recommendation** (Multi-Modal + CL) 2010 | 2011 | MM 2022, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3503161.3548294), [[Code]](https://github.com/georgeguo-cn/TopicVAE) 2012 | 2013 | 79. **Disentangled CVAEs with Contrastive Learning for Explainable Recommendation** (Explainable Rec + CL) 2014 | 2015 | AAAI 2023, [[PDF]](https://ojs.aaai.org/index.php/AAAI/article/view/26604/26376) 2016 | 2017 | 80. **DualVAE: Dual Disentangled Variational AutoEncoder for Recommendation** (Rec + CL) 2018 | 2019 | SIAM 2024, [[PDF]](https://arxiv.org/pdf/2401.04914.pdf), [[Code]](https://github.com/georgeguo-cn/DualVAE) 2020 | 2021 | 81. **Self-Supervised Learning for User Sequence Modeling** (Rec + CL) 2022 | 2023 | arXiv 2023, [[PDF]](https://sslneurips23.github.io/paper_pdfs/paper_39.pdf) 2024 | 2025 | 82. **RA-Rec: An Efficient ID Representation Alignment Framework for LLM-based Recommendation** (LLM + CL) 2026 | 2027 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2402.04527.pdf) 2028 | 2029 | 83. **CounterCLR: Counterfactual Contrastive Learning with Non-random Missing Data in Recommendation** (Counterfactual + CL) 2030 | 2031 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2402.05740.pdf) 2032 | 2033 | 84. **Non-autoregressive Generative Models for Reranking Recommendation** (Reranking + CL) 2034 | 2035 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2402.06871.pdf) 2036 | 2037 | 85. **Modeling Balanced Explicit and Implicit Relations with Contrastive Learning for Knowledge Concept Recommendation in MOOCs** (MOOC Rec + CL) 2038 | 2039 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2402.08256.pdf) 2040 | 2041 | 86. **MENTOR: Multi-level Self-supervised Learning for Multimodal Recommendation** (Multi-Modal + CL) 2042 | 2043 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2402.19407.pdf), [[Code]](https://github.com/Jinfeng-Xu/MENTOR) 2044 | 2045 | 87. **NoteLLM: A Retrievable Large Language Model for Note Recommendation** (Note Rec + CL) 2046 | 2047 | WWW 2024, [[PDF]](https://arxiv.org/pdf/2403.01744.pdf) 2048 | 2049 | 88. **A Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation** (Cross-Domain + CL) 2050 | 2051 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2403.03600.pdf) 2052 | 2053 | 89. **PPM : A Pre-trained Plug-in Model for Click-through Rate Prediction** (CTR + CL) 2054 | 2055 | WWW 2024, [[PDF]](https://arxiv.org/pdf/2403.10049.pdf) 2056 | 2057 | 90. **An Aligning and Training Framework for Multimodal Recommendations** (Multi-Modal + CL) 2058 | 2059 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2403.12384.pdf) 2060 | 2061 | 91. **Reinforcement Learning-based Recommender Systems with Large Language Models for State Reward and Action Modeling** (RL Rec + CL) 2062 | 2063 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2403.16948.pdf) 2064 | 2065 | 92. **Enhanced Generative Recommendation via Content and Collaboration Integration** (Generative Rec + CL) 2066 | 2067 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2403.18480.pdf) 2068 | 2069 | 93. **End-to-End Personalized Next Location Recommendation via Contrastive User Preference Modeling** (POI Rec + CL) 2070 | 2071 | arXiv 2023, [[PDF]](https://arxiv.org/pdf/2303.12507.pdf) 2072 | 2073 | 94. **Preference Aware Dual Contrastive Learning for Item Cold-Start Recommendation** (Cold Start + CL) 2074 | 2075 | AAAI 2024, [[PDF]](https://ojs.aaai.org/index.php/AAAI/article/view/28763/29465) 2076 | 2077 | 95. **Tail-STEAK: Improve Friend Recommendation for Tail Users via Self-Training Enhanced Knowledge Distillation** (Friend Rec + CL) 2078 | 2079 | AAAI 2024, [[PDF]](https://ojs.aaai.org/index.php/AAAI/article/view/28737/29421), [[Code]](https://github.com/antman9914/Tail-STEAK) 2080 | 2081 | 96. **Aiming at the Target: Filter Collaborative Information for Cross-Domain Recommendation** (Cross-Domain + CL) 2082 | 2083 | SIGIR 2024, [[PDF]](https://arxiv.org/pdf/2403.20296.pdf), [[Code]](https://anonymous.4open.science/r/CUT_anonymous-9815) 2084 | 2085 | 97. **Robust Federated Contrastive Recommender System against Model Poisoning Attack** (Fed Rec + CL) 2086 | 2087 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2403.20107.pdf), [[Code]](https://anonymous.4open.science/r/CUT_anonymous-9815) 2088 | 2089 | 98. **Bridging Language and Items for Retrieval and Recommendation** (Multi-Modal + CL) 2090 | 2091 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2403.03952.pdf), [[Code]](https://github.com/hyp1231/AmazonReviews2023) 2092 | 2093 | 99. **DRepMRec: A Dual Representation Learning Framework for Multimodal Recommendation** (Multi-Modal + CL) 2094 | 2095 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2404.11119.pdf) 2096 | 2097 | 100. **Knowledge-Aware Multi-Intent Contrastive Learning for Multi-Behavior Recommendation** (Multi-Behavior + CL) 2098 | 2099 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2404.11993.pdf) 2100 | 2101 | 101. **General Item Representation Learning for Cold-start Content Recommendations** (Cold Start + CL) 2102 | 2103 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2404.13808.pdf) 2104 | 2105 | 102. **MARec: Metadata Alignment for Cold-start Recommendation** (Cold Start + CL) 2106 | 2107 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2404.13298.pdf) 2108 | 2109 | 103. **Contrastive Quantization based Semantic Code for Generative Recommendation** (Generative Rec + CL) 2110 | 2111 | CIKM 2023, [[PDF]](https://arxiv.org/pdf/2404.14774.pdf) 2112 | 2113 | 104. **Retrieval-Oriented Knowledge for Click-Through Rate Prediction** (CTR + CL) 2114 | 2115 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2404.18304) 2116 | 2117 | 105. **Denoising Long-and Short-term Interests for Sequential Recommendation** (Session + DA + CL) 2118 | 2119 | SDM 2024, [[PDF]](https://epubs.siam.org/doi/pdf/10.1137/1.9781611978032.63), [[Code]](https://github.com/zxyllq/LSIDN) 2120 | 2121 | 106. **Learnable Tokenizer for LLM-based Generative Recommendation** (Gen Rec + CL) 2122 | 2123 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2405.07314) 2124 | 2125 | 107. **MVBIND: Self-Supervised Music Recommendation For Videos Via Embedding Space Binding** (Music Rec + CL) 2126 | 2127 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2405.09286) 2128 | 2129 | 108. **CELA: Cost-Efficient Language Model Alignment for CTR Prediction** (LLM + CTR + CL) 2130 | 2131 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2405.10596) 2132 | 2133 | 109. **A Unified Search and Recommendation Framework Based on Multi-Scenario Learning for Ranking in E-commerce** (Search & Rec + CL) 2134 | 2135 | SIGIR 2024, [[PDF]](https://arxiv.org/pdf/2405.10835) 2136 | 2137 | 110. **Learning Structure and Knowledge Aware Representation with Large Language Models for Concept Recommendation** (Concept Rec + CL) 2138 | 2139 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2405.12442) 2140 | 2141 | 111. **Bilateral Multi-Behavior Modeling for Reciprocal Recommendation in Online Recruitment** (Job Rec + CL) 2142 | 2143 | TKDE 2024, [[PDF]](https://ieeexplore.ieee.org/abstract/document/10521826/) 2144 | 2145 | 112. **Multi-Modal Recommendation Unlearning** (Rec Unlearning + CL) 2146 | 2147 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2405.15328) 2148 | 2149 | 113. **Your decision path does matter in pre-training industrial recommenders with multi-source behaviors** (Cross-Domain + CL) 2150 | 2151 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2405.17132) 2152 | 2153 | 114. **NoteLLM-2: Multimodal Large Representation Models for Recommendation** (Multi-Modal + CL) 2154 | 2155 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2405.16789) 2156 | 2157 | 115. **Multimodality Invariant Learning for Multimedia-Based New Item Recommendation** (Multi-Modal + CL) 2158 | 2159 | SIGIR 2024, [[PDF]](https://arxiv.org/pdf/2405.15783), [[Code]](https://github.com/HaoyueBai98/MILK) 2160 | 2161 | 116. **Cross-Domain LifeLong Sequential Modeling for Online Click-Through Rate Prediction** (CTR + CL) 2162 | 2163 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2312.06424) 2164 | 2165 | 117. **Medication Recommendation via Dual Molecular Modalities and Multi-Substructure Distillation** (Med Rec + CL) 2166 | 2167 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2405.20358) 2168 | 2169 | 118. **Item-Language Model for Conversational Recommendation** (Conversational Rec + CL) 2170 | 2171 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2406.02844) 2172 | 2173 | 119. **Boosting Multimedia Recommendation via Separate Generic and Unique Awareness** (Multi-Modal + CL) 2174 | 2175 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2406.08270), [[Code]](https://github.com/bruno686/SAND) 2176 | 2177 | 120. **Contextual Distillation Model for Diversified Recommendation** (Diversified Rec + CL) 2178 | 2179 | KDD 2024, [[PDF]](https://arxiv.org/pdf/2406.09021) 2180 | 2181 | 121. **DiffMM: Multi-Modal Diffusion Model for Recommendation** (Multi-Modal + DA + CL) 2182 | 2183 | MM 2024, [[PDF]](https://arxiv.org/pdf/2406.11781), [[Code]](https://github.com/HKUDS/DiffMM) 2184 | 2185 | 122. **Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User Feedback** (Multi-Modal + CL) 2186 | 2187 | KDD 2024, [[PDF]](https://arxiv.org/pdf/2406.12501), [[Code]](https://github.com/XMUDM/DA-MRS) 2188 | 2189 | 123. **EAGER: Two-Stream Generative Recommender with Behavior-Semantic Collaboration** (Gen Rec + CL) 2190 | 2191 | KDD 2024, [[PDF]](https://arxiv.org/pdf/2406.14017) 2192 | 2193 | 124. **Enhancing Collaborative Semantics of Language Model-Driven Recommendations via Graph-Aware Learning** (LLM Rec + CL) 2194 | 2195 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2406.13235) 2196 | 2197 | 125. **Hyperbolic Knowledge Transfer in Cross-Domain Recommendation System** (Cross-Domain + CL) 2198 | 2199 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2406.17289) 2200 | 2201 | 126. **MMBee: Live Streaming Gift-Sending Recommendations via Multi-Modal Fusion and Behaviour Expansion** (Gift-Sending Rec + CL) 2202 | 2203 | KDD 2024, [[PDF]](https://arxiv.org/pdf/2407.00056) 2204 | 2205 | 127. **Adapting Job Recommendations to User Preference Drift with Behavioral-Semantic Fusion Learning** (Job Rec + CL) 2206 | 2207 | KDD 2024, [[PDF]](https://arxiv.org/pdf/2407.00082) 2208 | 2209 | 128. **Personalised Outfit Recommendation via History-aware Transformers** (Outfit Rec + CL) 2210 | 2211 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2407.00289) 2212 | 2213 | 129. **Unified Dual-Intent Translation for Joint Modeling of Search and Recommendation** (Search & Rec + CL) 2214 | 2215 | KDD 2024, [[PDF]](https://arxiv.org/pdf/2407.00912), [[Code]](https://github.com/17231087/UDITSR) 2216 | 2217 | 130. **Language Models Encode Collaborative Signals in Recommendation** (LLM + Graph + CL) 2218 | 2219 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2407.05441), [[Code]](https://github.com/LehengTHU/AlphaRec) 2220 | 2221 | 131. **GUME: Graphs and User Modalities Enhancement for Long-Tail Multimodal Recommendation** (Multi-Modal + CL) 2222 | 2223 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2407.12338), [[Code]](https://github.com/NanGongNingYi/GUME) 2224 | 2225 | 132. **A Unified Graph Transformer for Overcoming Isolations in Multi-modal Recommendation** (Multi-Modal + CL) 2226 | 2227 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2407.19886) 2228 | 2229 | 133. **MOSAIC: Multimodal Multistakeholder-aware Visual Art Recommendation** (Art Rec + CL) 2230 | 2231 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2407.21758) 2232 | 2233 | 134. **Disentangled Contrastive Hypergraph Learning for Next POI Recommendation** (POI Rec + DA + CL) 2234 | 2235 | SIGIR 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3626772.3657726), [[Code]](https://github.com/icmpnorequest/SIGIR2024_DCHL) 2236 | 2237 | 135. **Modeling User Intent Beyond Trigger: Incorporating Uncertainty for Trigger-Induced Recommendation** (CTR + CL) 2238 | 2239 | CIKM 2024, [[PDF]](https://arxiv.org/pdf/2408.03091), [[Code]](https://github.com/majx1997/DUIN) 2240 | 2241 | 136. **SimCEN: Simple Contrast-enhanced Network for CTR Prediction** (CTR + CL) 2242 | 2243 | MM 2024, [[PDF]](https://openreview.net/pdf?id=pJHu4hDlLX), [[Code]](https://github.com/salmon1802/SimCEN) 2244 | 2245 | 137. **CETN: Contrast-enhanced Through Network for Click-Through Rate Prediction** (CTR + CL) 2246 | 2247 | TOIS 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3688571), [[Code]](https://github.com/salmon1802/CETN) 2248 | 2249 | 138. **Multi-task Heterogeneous Graph Learning on Electronic Health Records** (Drug Rec + CL) 2250 | 2251 | NN 2024, [[PDF]](https://arxiv.org/pdf/2408.07569), [[Code]](https://github.com/HKU-MedAI/MulT-EHR) 2252 | 2253 | 139. **Don’t Click the Bait: Title Debiasing News Recommendation via Cross-Field Contrastive Learning** (News Rec + CL) 2254 | 2255 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2408.08538) 2256 | 2257 | 140. **EasyRec: Simple yet Effective Language Models for Recommendation** (LLM + CL) 2258 | 2259 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2408.08821), [[Code]](https://github.com/HKUDS/EasyRec) 2260 | 2261 | 141. **Bundle Recommendation with Item-level Causation-enhanced Multi-view Learning** (Bundle Rec + CL) 2262 | 2263 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2408.08906) 2264 | 2265 | 142. **Debiased Contrastive Representation Learning for Mitigating Dual Biases in Recommender Systems** (Debias + CL) 2266 | 2267 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2408.09646) 2268 | 2269 | 143. **LARR: Large Language Model Aided Real-time Scene Recommendation with Semantic Understanding** (CTR + LLM + CL) 2270 | 2271 | RecSys 2024, [[PDF]](https://arxiv.org/pdf/2408.11523) 2272 | 2273 | 144. **Federated User Preference Modeling for Privacy-Preserving Cross-Domain Recommendation** (Cross-Domain + Fed Rec + CL) 2274 | 2275 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2408.14689), [[Code]](https://github.com/Lili1013/FUPM) 2276 | 2277 | 145. **Mitigating Negative Transfer in Cross-Domain Recommendation via Knowledge Transferability Enhancement** (Cross-Domain + CL) 2278 | 2279 | KDD 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3637528.3671799) 2280 | 2281 | 146. **Federated Prototype-based Contrastive Learning for Privacy-Preserving Cross-domain Recommendation** (Cross-Domain + CL) 2282 | 2283 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2409.03294) 2284 | 2285 | 147. **A Unified Framework for Cross-Domain Recommendation** (Cross-Domain + CL) 2286 | 2287 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2409.04540) 2288 | 2289 | 148. **End-to-End Learnable Item Tokenization for Generative Recommendation** (Gen Rec + CL) 2290 | 2291 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2409.05546) 2292 | 2293 | 149. **Towards Leveraging Contrastively Pretrained Neural Audio Embeddings for Recommender Tasks** (Music Rec + CL) 2294 | 2295 | RecSys 2024, [[PDF]](https://arxiv.org/pdf/2409.09026) 2296 | 2297 | 150. **A Multimodal Single-Branch Embedding Network for Recommendation in Cold-Start and Missing Modality Scenarios** (Multi-Modal + CL) 2298 | 2299 | RecSys 2024, [[PDF]](https://arxiv.org/pdf/2409.17864), [[Code]](https://github.com/hcai-mms/SiBraR---Single-Branch-Recommender) 2300 | 2301 | 151. **The Devil is in the Sources! Knowledge Enhanced Cross-Domain Recommendation in an Information Bottleneck Perspective** (Cross-Domain + CL) 2302 | 2303 | CIKM 2024, [[PDF]](https://arxiv.org/pdf/2409.19574) 2304 | 2305 | 152. **Contrastive Clustering Learning for Multi-Behavior Recommendation** (Multi-Behavior + CL) 2306 | 2307 | TOIS 2024, [[PDF]](https://dl.acm.org/doi/10.1145/3698192), [[Code]](https://github.com/lanbiolab/MBRCC) 2308 | 2309 | 153. **End-to-End Learnable Item Tokenization for Generative Recommendation** (Gen Rec + CL) 2310 | 2311 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2410.02939), [[Code]](https://github.com/Jamesding000/SpecGR) 2312 | 2313 | 154. **Improving Object Detection via Local-global Contrastive Learning** (OD + CL) 2314 | 2315 | BMVC 2024, [[PDF]](https://arxiv.org/pdf/2410.05058), [[Code]](https://local-global-detection.github.io/) 2316 | 2317 | 155. **DISCO: A Hierarchical Disentangled Cognitive Diagnosis Framework for Interpretable Job Recommendation** (Job Rec + CL) 2318 | 2319 | ICDM 2024, [[PDF]](https://arxiv.org/pdf/2410.07671), [[Code]](https://github.com/LabyrinthineLeo/DISCO) 2320 | 2321 | 156. **Neural Contrast: Leveraging Generative Editing for Graphic Design Recommendations** (Design Rec + CL) 2322 | 2323 | PRICAI 2024, [[PDF]](https://arxiv.org/pdf/2410.07211) 2324 | 2325 | 157. **Pseudo Dataset Generation for Out-of-domain Multi-Camera View Recommendation** (View Rec + CL) 2326 | 2327 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2410.13585) 2328 | 2329 | 158. **Hyperbolic Contrastive Learning for Cross-Domain Recommendation** (Cross-Domain + CL) 2330 | 2331 | CIKM 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3627673.3679572), [[Code]](https://github.com/EnkiXin/hcts) 2332 | 2333 | 159. **Enhancing CTR prediction in Recommendation Domain with Search Query Representation** (CTR + CL) 2334 | 2335 | CIKM 2024, [[PDF]](https://arxiv.org/pdf/2410.21487) 2336 | 2337 | 160. **Multi-Modal Correction Network for Recommendation** (Multi-Modal + CL) 2338 | 2339 | TKDE 2024, [[PDF]](https://ieeexplore.ieee.org/document/10746604) 2340 | 2341 | 161. **QARM: Quantitative Alignment Multi-Modal Recommendation at Kuaishou** (Multi-Modal + CL) 2342 | 2343 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2411.11739) 2344 | 2345 | 162. **Collaborative Contrastive Network for Click-Through Rate Prediction** (CTR + CL) 2346 | 2347 | arXiv 2024, [[PDF]](https://arxiv.org/pdf/2411.11508) 2348 | 2349 | 163. **Hierarchical Denoising for Robust Social Recommendation** (Social Rec + CL) 2350 | 2351 | TKDE 2024, [[PDF]](https://ieeexplore.ieee.org/document/10771708) 2352 | 2353 | 164. **Learning Self-Supervised Audio-Visual Representations for Sound Recommendations** (Sound Rec + CL) 2354 | 2355 | ISVC 2021, [[PDF]](https://arxiv.org/pdf/2412.07406) 2356 | 2357 | 165. **Bridging the User-side Knowledge Gap in Knowledge-aware Recommendations with Large Language Models** (Knowledge-aware Rec + LLM + CL) 2358 | 2359 | AAAI 2025, [[PDF]](https://arxiv.org/pdf/2412.13544), [[Code]](https://github.com/laowangzi/CIKGRec) 2360 | 2361 | 166. **TCKT: Tree-Based Cross-domain Knowledge Transfer for Next POI Cold-Start Recommendation** (POI Rec + CL) 2362 | 2363 | TOIS 2024, [[PDF]](https://dl.acm.org/doi/pdf/10.1145/3709137), [[Code]](https://github.com/simplehx/TCKT) 2364 | 2365 | 167. **Contrastive Representation for Interactive Recommendation** (Interactive Rec + CL) 2366 | 2367 | AAAI 2025, [[PDF]](https://arxiv.org/pdf/2412.18396) 2368 | 2369 | 168. **MixMBR: Contrastive Learning for Multi-behavior Recommendation** (Multi-Behavior + DA + CL) 2370 | 2371 | DASFAA 2023, [[PDF]](https://link.springer.com/chapter/10.1007/978-3-031-30672-3_29) 2372 | 2373 | 169. **Content-Based Collaborative Generation for Recommender Systems** (Generative Rec + LLM + CL) 2374 | 2375 | CIKM 2024, [[PDF]](https://dl.acm.org/doi/10.1145/3627673.3679692), [[Code]](https://github.com/Junewang0614/ColaRec) 2376 | 2377 | 170. **A Contrastive Pretrain Model with Prompt Tuning for Multi-center Medication Recommendation** (Medication Rec + CL) 2378 | 2379 | TOIS 2024, [[PDF]](https://arxiv.org/pdf/2412.20040), [[Code]](https://github.com/Applied-Machine-Learning-Lab/TEMPT) 2380 | 2381 | 171. **C2lRec: Causal Contrastive Learning for User Cold-start Recommendation with Social Variable** (Cold Start + CL) 2382 | 2383 | TOIS 2025, [[PDF]](https://dl.acm.org/doi/10.1145/3711858), [[Code]](https://github.com/Applied-Machine-Learning-Lab/TEMPT) 2384 | 2385 | 172. **Dual Enhanced Meta-learning with Adaptive Task Scheduler for Cold-Start Recommendation** (Cold Start + CL) 2386 | 2387 | TKDE 2025, [[PDF]](https://ieeexplore.ieee.org/document/10840305/) 2388 | 2389 | 173. **Disentangled Modeling of Preferences and Social Influence for Group Recommendation** (Group Rec + CL) 2390 | 2391 | AAAI 2025, [[PDF]](https://arxiv.org/pdf/2501.11342), [[Code]](https://github.com/DisRec/DisRec) 2392 | 2393 | 174. **Generating with Fairness: A Modality-Diffused Counterfactual Framework for Incomplete Multimodal Recommendations** (Multi-Modal + CL) 2394 | 2395 | WWW 2025, [[PDF]](https://arxiv.org/pdf/2501.11916), [[Code]](https://github.com/JinLi-i/MoDiCF) 2396 | 2397 | 175. **MVideoRec: Micro Video Recommendations Through Modality Decomposition and Contrastive Learning** (Multi-Modal + DA + CL) 2398 | 2399 | TOIS 2025, [[PDF]](https://dl.acm.org/doi/10.1145/3711855) 2400 | 2401 | 176. **Enhancing Reranking for Recommendation with LLMs through User Preference Retrieval** (Reranking + CL) 2402 | 2403 | COLING 2025, [[PDF]](https://aclanthology.org/2025.coling-main.45/) 2404 | 2405 | 177. **Ownership Verification for Federated Recommendation** (Federated Rec + CL) 2406 | 2407 | TOIS 2025, [[PDF]](https://dl.acm.org/doi/10.1145/3715320) 2408 | 2409 | 178. **Contrastive Modality-Disentangled Learning for Multimodal Recommendation** (Multi-Modal + CL) 2410 | 2411 | TOIS 2025, [[PDF]](https://dl.acm.org/doi/10.1145/3715876), [[Code]](https://github.com/ruiliu2020/CMDL) 2412 | 2413 | 179. **Generating Negative Samples for Multi-Modal Recommendation** (Multi-Modal + CL) 2414 | 2415 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2501.15183) 2416 | 2417 | 180. **Combinatorial Optimization Perspective based Framework for Multi-behavior Recommendation** (Multi-Behavior + CL) 2418 | 2419 | KDD 2025, [[PDF]](https://arxiv.org/pdf/2502.02232), [[Code]](https://github.com/1918190/COPF) 2420 | 2421 | 181. **Large Language Models Are Universal Recommendation Learners** (LLM + CL) 2422 | 2423 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2502.03041), [[Code]](https://github.com/1918190/COPF) 2424 | 2425 | 182. **Intent Representation Learning with Large Language Model for Recommendation** (LLM + CL) 2426 | 2427 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2502.03307), [[Code]](https://github.com/wangyu0627/IRLLRec) 2428 | 2429 | 183. **Contrastive Learning for Cold Start Recommendationwith Adaptive Feature Fusion** (Cold Start + CL) 2430 | 2431 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2502.03664) 2432 | 2433 | 184. **Progressive Collaborative and Semantic Knowledge Fusion for Generative Recommendation** (Generative Rec + CL) 2434 | 2435 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2502.06269) 2436 | 2437 | 185. **FARM: Frequency-Aware Model for Cross-Domain Live-Streaming Recommendation** (Cross-Domain + CL) 2438 | 2439 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2502.09375) 2440 | 2441 | 186. **CoST: Contrastive Quantization based Semantic Tokenization for Generative Recommendation** (Generative Rec + CL) 2442 | 2443 | RecSys 2024, [[PDF]](https://arxiv.org/pdf/2404.14774) 2444 | 2445 | 187. **EAGER-LLM: Enhancing Large Language Models as Recommenders through Exogenous Behavior-Semantic Integration** (LLM + CL) 2446 | 2447 | WWW 2025, [[PDF]](https://arxiv.org/pdf/2502.14735) 2448 | 2449 | 188. **A Universal Framework for Compressing Embeddings in CTR Prediction** (CTR + CL) 2450 | 2451 | DASFAA 2025, [[PDF]](https://arxiv.org/pdf/2502.15355), [[Code]](https://github.com/USTC-StarTeam/MEC) 2452 | 2453 | 189. **Joint Similarity Item Exploration and Overlapped User Guidance for Multi-Modal Cross-Domain Recommendation** (Multi-Modal + Cross-Domain + CL) 2454 | 2455 | WWW 2025, [[PDF]](https://arxiv.org/pdf/2502.16068) 2456 | 2457 | 190. **Separated Contrastive Learning for Matching in Cross-domain Recommendation with Curriculum Scheduling** (Cross-Domain + CL) 2458 | 2459 | WWW 2025, [[PDF]](https://arxiv.org/pdf/2502.16239) 2460 | 2461 | 191. **Social Relation Meets Recommendation: Denoising and Alignment** (Social Rec + CL) 2462 | 2463 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2502.15695) 2464 | 2465 | 192. **Enhancing Recommender Systems: Deep Modality Alignment with Large Multi-Modal Encoders** (Multi-Modal + CL) 2466 | 2467 | TORS 2025, [[PDF]](https://dl.acm.org/doi/abs/10.1145/3718099), [[Code]](https://github.com/zxy-ml84/LMM4Rec/) 2468 | 2469 | 193. **MDE: Modality Discrimination Enhancement for Multi-modal Recommendation** (Multi-Modal + CL) 2470 | 2471 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2502.18481) 2472 | 2473 | 194. **Multiview Graph Dual-Attention Deep Learning and Contrastive Learning for Multi-Criteria Recommender Systems** (Multi-Criteria Rec + CL) 2474 | 2475 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2502.19271) 2476 | 2477 | 195. **Hierarchical Gating Network for Cross-Domain Sequential Recommendation** (Cross-Domain + Sequential+ CL) 2478 | 2479 | TOIS 2025, [[PDF]](https://dl.acm.org/doi/10.1145/3715321), [[Code]](https://github.com/solozhu/hgncdsr) 2480 | 2481 | 196. **Intrinsic and Extrinsic Factor Disentanglement for Recommendation in Various Context Scenarios** (Factor Disentanglement + CL) 2482 | 2483 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2503.03524), [[Code]](https://github.com/ethanmock/IEDR) 2484 | 2485 | 197. **Federated Cross-Domain Click-Through Rate Prediction With Large Language Model Augmentation** (CTR + LLM + DA + CL) 2486 | 2487 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2503.16875) 2488 | 2489 | 198. **Learning Human Feedback from Large Language Models for Content Quality-aware Recommendation** (LLM + CL) 2490 | 2491 | TOIS 2025, [[PDF]](https://dl.acm.org/doi/10.1145/3727144), [[Code]](https://github.com/wanghl21/HFAR) 2492 | 2493 | 199. **Learning Human Feedback from Large Language Models for Content Quality-aware Recommendation** (Search & Rec + CL) 2494 | 2495 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2504.06714) 2496 | 2497 | 200. **Generative Recommendation with Continuous-Token Diffusion** (Generative Rec + CL) 2498 | 2499 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2504.12007) 2500 | 2501 | 201. **Consensus-aware Contrastive Learning for Group Recommendation** (Group Rec + DA + CL) 2502 | 2503 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2504.13703) 2504 | 2505 | 202. **A Reinforcement Learning Method to Factual and Counterfactual Explanations for Session-based Recommendation** (Session Rec + DA + CL) 2506 | 2507 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2504.13632) 2508 | 2509 | 203. **Disentangling and Generating Modalities for Recommendation in Missing Modality Scenarios** (Multi-Modal + CL) 2510 | 2511 | SIGIR 2025, [[PDF]](https://arxiv.org/pdf/2504.16352), [[Code]](https://github.com/ptkjw1997/DGMRec) 2512 | 2513 | 204. **Beyond Whole Dialogue Modeling: Contextual Disentanglement for Conversational Recommendation** (Conversational Rec + CL) 2514 | 2515 | SIGIR 2025, [[PDF]](https://arxiv.org/pdf/2504.17427) 2516 | 2517 | 205. **Combating the Bucket Effect: Multi-Knowledge Alignment for Medication Recommendation** (Medication Rec + CL) 2518 | 2519 | arXiv 2025, [[PDF]](https://arxiv.org/pdf/2504.18096), [[Code]](https://github.com/MKMed-2025/MKMed) 2520 | 2521 | 206. **Mining Linguistic Styles in Bilateral Matching: A Contrastive Learning Approach to Reciprocal Recommendation** (Reciprocal Rec + CL) 2522 | 2523 | TKDD 2025, [[PDF]](https://dl.acm.org/doi/10.1145/3736418) 2524 | 2525 | 207. **User Invariant Preference Learning for Multi-Behavior Recommendation** (Multi-Behavior Rec + CL) 2526 | 2527 | TOIS 2025, [[PDF]](https://dl.acm.org/doi/10.1145/3728465), [[Code]](https://github.com/MingshiYan/UIPL) 2528 | --------------------------------------------------------------------------------