├── LICENSE └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2021 Zilize 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # CRS Papers 2 | 3 | ![](https://img.shields.io/github/last-commit/Zilize/CRSPapers?color=blue) ![](https://img.shields.io/badge/PaperNumber-89-brightgreen) ![](https://img.shields.io/badge/PRs-Welcome-red) 4 | 5 | A Conversational Recommender System (CRS) is defined by [Gao et al. (2021)](https://arxiv.org/pdf/2101.09459.pdf) as following: 6 | 7 | > *A recommendation system that can elicit the dynamic preferences of users and take actions based on their current needs through real-time multi-turn interactions using natural language.* 8 | 9 | ### Contents 10 | 11 | - [Quick-Start](#Quick-Start) 12 | - [Survey and Tutorial](#Survey-and-Tutorial) 13 | - [Survey](#Survey) 14 | - [Tutorial](#Tutorial) 15 | - [Toolkit and Dataset](#Toolkit-and-Dataset) 16 | - [Toolkit](#Toolkit) 17 | - [Dataset](#Dataset) 18 | - [Model](#Model) 19 | - [Attribute-based](#Attribute-based) 20 | - [Generation-based](#Generation-based) 21 | - [Others](#Others) 22 | - [Other](#Other) 23 | - [Thesis](#Thesis) 24 | 25 | 26 | 27 | ## Quick-Start 28 | 29 | > A quick-start paper list including survey, tutorial, toolkit and model papers. 30 | 31 | 1. "Deep Conversational Recommender Systems: A New Frontier for Goal-Oriented Dialogue Systems". `arXiv(2020)` [[PDF]](https://arxiv.org/pdf/2004.13245.pdf) 32 | 2. "Tutorial on Conversational Recommendation Systems". `RecSys(2020)` [[PDF]](http://yongfeng.me/attach/fu-recsys2020.pdf) [[Homepage]](https://conversational-recsys.github.io/) 33 | 3. **CRSLab**: "CRSLab: An Open-Source Toolkit for Building Conversational Recommender System". `ACL(2021)` [[PDF]](https://arxiv.org/pdf/2101.00939.pdf) [[Homepage]](https://github.com/RUCAIBox/CRSLab) 34 | 4. **CRM**: "Conversational Recommender System". `SIGIR(2018)` [[PDF]](https://arxiv.org/pdf/1806.03277) [[Homepage]](https://github.com/ysun30/ConvRec) 35 | 5. **SAUR**: "Towards Conversational Search and Recommendation: System Ask, User Respond". `CIKM(2018)` [[PDF]](https://par.nsf.gov/servlets/purl/10090082) [[Dataset]](http://yongfeng.me/attach/conversation.zip) 36 | 6. **EAR**: "Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems". `WSDM(2020)` [[PDF]](https://arxiv.org/pdf/2002.09102) [[Homepage]](https://ear-conv-rec.github.io/) 37 | 7. **CPR**: "Interactive Path Reasoning on Graph for Conversational Recommendation". `KDD(2020)` [[PDF]](https://arxiv.org/pdf/2007.00194) [[Homepage]](https://cpr-conv-rec.github.io/) 38 | 8. **ReDial**: "Towards Deep Conversational Recommendations". `NeurIPS(2018)` [[PDF]](https://arxiv.org/pdf/1812.07617) [[Dataset]](https://redialdata.github.io/website/) [[Code]](https://github.com/RaymondLi0/conversational-recommendations) 39 | 9. **KBRD**: "Towards Knowledge-Based Recommender Dialog System". `EMNLP-IJCNLP(2019)` [[PDF]](https://arxiv.org/pdf/1908.05391.pdf) [[Code]](https://github.com/THUDM/KBRD) 40 | 41 | 10. **KGSF**: "Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion". `KDD(2020)` [[PDF]](https://arxiv.org/pdf/2007.04032) [[Code]](https://github.com/Lancelot39/KGSF) 42 | 43 | 44 | 45 | ## Survey and Tutorial 46 | 47 | ### Survey 48 | 49 | 1. "Deep Conversational Recommender Systems: A New Frontier for Goal-Oriented Dialogue Systems". `arXiv(2020)` [[PDF]](https://arxiv.org/pdf/2004.13245.pdf) 50 | 2. "A survey on conversational recommender systems". `arXiv(2020)` [[PDF]](https://arxiv.org/pdf/2004.00646.pdf) 51 | 52 | 3. "Advances and Challenges in Conversational Recommender Systems: A Survey". `arXiv(2021)` [[PDF]](https://arxiv.org/pdf/2101.09459.pdf) 53 | 54 | ### Tutorial 55 | 56 | 1. "Tutorial on Conversational Recommendation Systems". [[Homepage]](https://conversational-recsys.github.io/) 57 | - `RecSys(2020)` [[PDF]](http://yongfeng.me/attach/fu-recsys2020.pdf) 58 | - `WSDM(2021)` [[PDF]](http://yongfeng.me/attach/fu-wsdm2021.pdf) 59 | - `IUI(2021)` [[PDF]](http://yongfeng.me/attach/fu-iui2021.pdf) 60 | 61 | 2. "Conversational Recommendation: Formulation, Methods, and Evaluation". `SIGIR(2020)` [[PDF]](http://staff.ustc.edu.cn/~hexn/papers/sigir20-tutorial.pdf) [[Slides]](http://staff.ustc.edu.cn/~hexn/slides/sigir20-tutorial-CRS-slides.pdf) 62 | 63 | 64 | 65 | ## Toolkit and Dataset 66 | 67 | ### Toolkit 68 | 69 | 1. **CRSLab**: "CRSLab: An Open-Source Toolkit for Building Conversational Recommender System". `ACL(2021)` [[PDF]](https://arxiv.org/pdf/2101.00939.pdf) [[Homepage]](https://github.com/RUCAIBox/CRSLab) 70 | 71 | ### Dataset 72 | 73 | 1. **ConvRec**: "Conversational Recommender System". `SIGIR(2018)` [[PDF]](https://arxiv.org/pdf/1806.03277) [[Homepage]](https://github.com/ysun30/ConvRec) 74 | 75 | 2. **SAUR**: "Towards Conversational Search and Recommendation: System Ask, User Respond". `CIKM(2018)` [[PDF]](https://par.nsf.gov/servlets/purl/10090082) [[Download]](http://yongfeng.me/attach/conversation.zip) 76 | 3. **EAR**: "Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems". `WSDM(2020)` [[PDF]](https://arxiv.org/pdf/2002.09102) [[Homepage]](https://ear-conv-rec.github.io/) 77 | 4. **CPR**: "Interactive Path Reasoning on Graph for Conversational Recommendation". `KDD(2020)` [[PDF]](https://arxiv.org/pdf/2007.00194) [[Homepage]](https://cpr-conv-rec.github.io/) 78 | 5. **ReDial**: "Towards Deep Conversational Recommendations". `NeurIPS(2018)` [[PDF]](https://arxiv.org/pdf/1812.07617) [[Homepage]](https://redialdata.github.io/website/) 79 | 6. **OpenDialKG**: "OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs". `ACL(2019)` [[PDF]](https://www.aclweb.org/anthology/P19-1081.pdf) [[Homepage]](https://github.com/facebookresearch/opendialkg) 80 | 7. **PersuasionForGood**: "Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good". `ACL(2019)` [[PDF]](https://arxiv.org/pdf/1906.06725.pdf) [[Homepage]](https://gitlab.com/ucdavisnlp/persuasionforgood) 81 | 8. **CCPE**: "Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences". `SIGDial(2019)` [[PDF]](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/54521b4011d0c2a19eaade8005ff4a499f754301.pdf) [[Homepage]](https://github.com/google-research-datasets/ccpe) 82 | 9. **TG-ReDial**: "Towards Topic-Guided Conversational Recommender System". `COLING(2020)` [[PDF]](https://arxiv.org/pdf/2010.04125) [[Homepage]](https://github.com/RUCAIBox/TG-ReDial) 83 | 10. **GoRecDial**: "Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue". `EMNLP(2019)` [[PDF]](https://arxiv.org/pdf/1909.03922) [[Download]](https://drive.google.com/drive/folders/1nilk6FUktW2VjNlATdM0VMehzSOPIvJ0?usp=sharing) 84 | 11. **DuRecDial**: "Towards Conversational Recommendation over Multi-Type Dialogs". `ACL(2020)` [[PDF]](https://arxiv.org/pdf/2005.03954) [[Download]](https://baidu-nlp.bj.bcebos.com/DuRecDial.zip) 85 | 12. **INSPIRED**: "INSPIRED: Toward Sociable Recommendation Dialogue Systems". `EMNLP(2020)` [[PDF]](https://www.aclweb.org/anthology/2020.emnlp-main.654.pdf) [[Homepage]](https://github.com/sweetpeach/Inspired) 86 | 13. **MGConvRex**: "User Memory Reasoning for Conversational Recommendation". `ACL(2020)` [[PDF]](https://arxiv.org/pdf/2006.00184) 87 | 14. **COOKIE**: "COOKIE: A Dataset for Conversational Recommendation over Knowledge Graphs in E-commerce". `arXiv(2020)` [[PDF]](https://arxiv.org/pdf/2008.09237) [[Homepage]](https://github.com/zuohuif/COOKIE) 88 | 15. **IARD**: "Predicting User Intents and Satisfaction with Dialogue-based Conversational Recommendations". `UMAP(2020)` [[PDF]](http://www.comp.hkbu.edu.hk/~lichen/download/Cai_UMAP20.pdf) [[Homepage]](https://wanlingcai.github.io/files/2020/UMAP2020_dataset_readme.html) 89 | 16. **DuRecDial 2.0**: "DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation". `EMNLP(2021)` [[PDF]](https://arxiv.org/pdf/2109.08877.pdf) [[Homepage]](https://github.com/liuzeming01/DuRecDial) 90 | 17. **MMConv**: "MMConv: An Environment for Multimodal Conversational Search across Multiple Domains". `SIGIR(2021)` [[PDF]](https://liziliao.github.io/papers/2021sigir_mmconv.pdf) [[Homepage]](https://github.com/liziliao/MMConv) 91 | 18. **INSPIRED2**: "INSPIRED2: An Improved Dataset for Sociable Conversational Recommendation." `RecSys(2022)` [[PDF]](https://arxiv.org/pdf/2208.04104.pdf) [[Homepage]](https://github.com/ahtsham58/INSPIRED2) 92 | 93 | 94 | ## Model 95 | 96 | ### Attribute-based 97 | 98 | > Attribute-based CRSs typically capture user preferences by asking queries about item attributes and generates responses using pre-defined templates. 99 | 100 | 1. "Towards Conversational Recommender Systems". `KDD(2016)` [[PDF]](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/rfp0063-christakopoulou.pdf) 101 | 2. **CRM**: "Conversational Recommender System". `SIGIR(2018)` [[PDF]](https://arxiv.org/pdf/1806.03277) [[Homepage]](https://github.com/ysun30/ConvRec) 102 | 3. **SAUR**: "Towards Conversational Search and Recommendation: System Ask, User Respond". `CIKM(2018)` [[PDF]](https://par.nsf.gov/servlets/purl/10090082) [[Dataset]](http://yongfeng.me/attach/conversation.zip) 103 | 4. **Q&R**: "Q&R: A Two-Stage Approach toward Interactive Recommendation". `KDD(2018)` [[PDF]](http://www.alexbeutel.com/papers/q-and-r-kdd2018.pdf) 104 | 5. "Dialogue based recommender system that flexibly mixes utterances and recommendations". `WI(2019)` [[Link]](https://ieeexplore.ieee.org/abstract/document/8909617) 105 | 6. **EAR**: "Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems". `WSDM(2020)` [[PDF]](https://arxiv.org/pdf/2002.09102) [[Homepage]](https://ear-conv-rec.github.io/) 106 | 7. **CPR**: "Interactive Path Reasoning on Graph for Conversational Recommendation". `KDD(2020)` [[PDF]](https://arxiv.org/pdf/2007.00194) [[Homepage]](https://cpr-conv-rec.github.io/) 107 | 8. **CRSAL**: "CRSAL: Conversational Recommender Systems with Adversarial Learning". `TOIS(2020)` [[PDF]](https://repository.kaust.edu.sa/bitstream/handle/10754/665725/TOIS.pdf?sequence=1&isAllowed=y) [[Code]](https://github.com/XuhuiRen/CRSAL) 108 | 9. **Qrec**: "Towards Question-Based Recommender Systems". `SIGIR(2020)` [[PDF]](https://arxiv.org/pdf/2005.14255.pdf) [[Code]](https://github.com/JieZouIR/Qrec) 109 | 10. **ConTS**: "Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users". `TOIS(2021)` [[PDF]](https://arxiv.org/pdf/2005.12979) [[Code]](https://github.com/xiwenchao/conTS-TOIS-2021) 110 | 11. **UNICORN**: "Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning". `SIGIR(2021)` [[PDF]](https://arxiv.org/pdf/2105.09710.pdf) [[Code]](https://github.com/dengyang17/unicorn) 111 | 12. **KBQG**: "Learning to Ask Appropriate Questions in Conversational Recommendation". `arXiv(2021)` [[PDF]](https://arxiv.org/pdf/2105.04774.pdf) [[Code]](https://github.com/XuhuiRen/KBQG) 112 | 13. **FPAN**: "Adapting User Preference to Online Feedback in Multi-round Conversational Recommendation". `WSDM(2021)` [[Link]](https://dl.acm.org/doi/abs/10.1145/3437963.3441791) [[Code]](https://github.com/xxkkrr/FPAN) 113 | 114 | 14. "Developing a Conversational Recommendation System for Navigating Limited Options". `CHI(2021)` [[PDF]](https://arxiv.org/pdf/2104.06552.pdf) 115 | 15. **MCMIPL**: "Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation." `WWW(2022)` [[PDF]](https://arxiv.org/pdf/2112.11775.pdf) [[Code]](https://github.com/ZYM6-6/MCMIPL) 116 | 16. "Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems." `CIKM(2022)` [[PDF]](https://arxiv.org/pdf/2208.03298.pdf) 117 | 17. **MINICORN**: "Minimalist and High-performance Conversational Recommendation with Uncertainty Estimation for User Preference." `arXiv(2022)` [[PDF]](https://arxiv.org/pdf/2206.14468.pdf) 118 | 18. **CRIF**: "Learning to Infer User Implicit Preference in Conversational Recommendation." `SIGIR(2022)` [[PDF]](https://dl.acm.org/doi/abs/10.1145/3477495.3531844) 119 | 19. **HICR**: "Conversational Recommendation via Hierarchical Information Modeling." `SIGIR(2022)` [[PDF]](https://dl.acm.org/doi/abs/10.1145/3477495.3531830) 120 | 20. **MetaCRS**: "Meta Policy Learning for Cold-Start Conversational Recommendation." `WSDM(2023)` [[PDF]](https://arxiv.org/pdf/2205.11788.pdf) 121 | 122 | ### Generation-based 123 | 124 | > Compared to attribute-based CRSs, generation-based CRSs pay more attention to generate human-like responses in natural language. 125 | 126 | 1. **ReDial**: "Towards Deep Conversational Recommendations". `NeurIPS(2018)` [[PDF]](https://arxiv.org/pdf/1812.07617) [[Code]](https://github.com/RaymondLi0/conversational-recommendations) [[Dataset]](https://redialdata.github.io/website/) 127 | 128 | 2. **KBRD**: "Towards Knowledge-Based Recommender Dialog System". `EMNLP-IJCNLP(2019)` [[PDF]](https://arxiv.org/pdf/1908.05391.pdf) [[Code]](https://github.com/THUDM/KBRD) 129 | 3. **GoRecDial**: "Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue". `EMNLP(2019)` [[PDF]](https://arxiv.org/pdf/1909.03922) [[Code]](https://github.com/facebookresearch/ParlAI) [[Dataset]](https://drive.google.com/drive/folders/1nilk6FUktW2VjNlATdM0VMehzSOPIvJ0?usp=sharing) 130 | 4. **DialKG Walker**: "OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs". `ACL(2019)` [[PDF]](https://www.aclweb.org/anthology/P19-1081.pdf) [[Code]](https://github.com/madcpt/OpenDialKG) [[Dataset]](https://github.com/facebookresearch/opendialkg) 131 | 5. **DCR**: "Deep Conversational Recommender in Travel". `TKDE(2020)` [[PDF]](https://arxiv.org/pdf/1907.00710.pdf) [[Code]](https://github.com/truthless11/DCR) 132 | 6. **KGSF**: "Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion". `KDD(2020)` [[PDF]](https://arxiv.org/pdf/2007.04032) [[Code]](https://github.com/Lancelot39/KGSF) 133 | 7. **MGCG**: "Towards Conversational Recommendation over Multi-Type Dialogs". `ACL(2020)` [[PDF]](https://arxiv.org/pdf/2005.03954.pdf) [[Code]](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/Research/ACL2020-DuRecDial) [[Dataset]](https://baidu-nlp.bj.bcebos.com/DuRecDial.zip) 134 | 8. **ECR**: "Towards Explainable Conversational Recommendation". `IJCAI(2020)` [[PDF]](https://www.ijcai.org/Proceedings/2020/0414.pdf) 135 | 9. **INSPIRED**: "INSPIRED: Toward Sociable Recommendation Dialogue Systems". `EMNLP(2020)` [[PDF]](https://www.aclweb.org/anthology/2020.emnlp-main.654.pdf) [[Homepage]](https://github.com/sweetpeach/Inspired) 136 | 10. **TG-ReDial**: "Towards Topic-Guided Conversational Recommender System". `COLING(2020)` [[PDF]](https://arxiv.org/pdf/2010.04125) [[Homepage]](https://github.com/RUCAIBox/TG-ReDial) 137 | 11. **MGConvRex**: "User Memory Reasoning for Conversational Recommendation". `COLING(2020)` [[PDF]](https://arxiv.org/pdf/2006.00184) 138 | 12. **KGConvRec**: "Suggest me a movie for tonight: Leveraging Knowledge Graphs for Conversational Recommendation". `COLING(2020)` [[PDF]](https://www.aclweb.org/anthology/2020.coling-main.369.pdf) [[Code]](https://github.com/rajbsk/KG-conv-rec) 139 | 13. **CR-Walker**: "Bridging the Gap between Conversational Reasoning and Interactive Recommendation". `arXiv(2020)` [[PDF]](https://arxiv.org/pdf/2010.10333.pdf) [[Code]](https://github.com/truthless11/CR-Walker) 140 | 14. **RevCore**: "RevCore: Review-augmented Conversational Recommendation". `ACL-Findings(2021)` [[PDF]](https://arxiv.org/pdf/2106.00957.pdf) [[Code]](https://github.com/JD-AI-Research-NLP/RevCore) 141 | 15. **KECRS**: "KECRS: Towards Knowledge-Enriched Conversational Recommendation System". `arXiv(2021)` [[PDF]](https://arxiv.org/pdf/2105.08261.pdf) 142 | 16. "Category Aware Explainable Conversational Recommendation". `arXiv(2021)` [[PDF]](https://arxiv.org/pdf/2103.08733.pdf) 143 | 17. **DuRecDial 2.0**: "DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation". `EMNLP(2021)` [[PDF]](https://arxiv.org/pdf/2109.08877.pdf) [[Dataset]](https://github.com/liuzeming01/DuRecDial) 144 | 18. **NTRD**: "Learning Neural Templates for Recommender Dialogue System." `EMNLP(2021)` [[PDF]](https://arxiv.org/pdf/2109.12302.pdf) [[Code]](https://github.com/jokieleung/NTRD) 145 | 19. **CRFR**: "CRFR: Improving Conversational Recommender Systems via Flexible Fragments Reasoning on Knowledge Graphs." `EMNLP(2021)` [[PDF]](https://aclanthology.org/2021.emnlp-main.355.pdf) 146 | 20. **RID**: "Finetuning Large-Scale Pre-trained Language Models for Conversational Recommendation with Knowledge Graph." `arXiv(2021)` [[PDF]](https://arxiv.org/pdf/2110.07477.pdf) [[Code]](https://github.com/Lingzhi-WANG/PLM-BasedCRS) 147 | 21. **RecInDial**: "RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models." `AACL(2022)` [[PDF]](https://arxiv.org/pdf/2110.07477.pdf) [[Code]](https://github.com/Lingzhi-WANG/PLM-BasedCRS) 148 | 22. **MESE**: "Improving Conversational Recommendation Systems’ Quality with Context-Aware Item Meta Information." `NAACL(2022)` [[PDF]](https://arxiv.org/pdf/2112.08140.pdf) [[Code]](https://github.com/by2299/MESE) 149 | 23. **C2-CRS**: "C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System." `WSDM(2022)` [[PDF]](https://arxiv.org/pdf/2201.02732.pdf) [[Code]](https://github.com/RUCAIBox/WSDM2022-C2CRS) 150 | 24. **BARCOR**: "BARCOR: Towards A Unified Framework for Conversational Recommendation Systems." `arXiv(2022)` [[PDF]](https://arxiv.org/pdf/2203.14257.pdf) 151 | 25. **UniMIND**: "A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems." `TOIS(2023)` [[PDF]](https://arxiv.org/pdf/2204.06923.pdf) [[Code]](https://github.com/dengyang17/unimind) 152 | 26. **UCCR**: "User-Centric Conversational Recommendation with Multi-Aspect User Modeling." `SIGIR(2022)` [[PDF]](https://arxiv.org/pdf/2204.09263.pdf) [[Code]](https://github.com/lisk123/UCCR) 153 | 27. **UPCR**: "Variational Reasoning about User Preferences for Conversational Recommendation." `SIGIR(2022)` [[PDF]](https://staff.fnwi.uva.nl/m.derijke/wp-content/papercite-data/pdf/ren-2022-variational.pdf) [[Code]](https://github.com/tianz2020/UPCR) 154 | 28. **TSCR**: "Improving Conversational Recommender Systems via Transformer-based Sequential Modelling." `SIGIR(2022)` [[PDF]](https://dl.acm.org/doi/abs/10.1145/3477495.3531852) 155 | 29. **CCRS**: "Customized Conversational Recommender Systems." `ECML-PKDD(2022)` [[PDF]](https://arxiv.org/pdf/2207.00814.pdf) 156 | 30. **UniCRS**: "Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning." `KDD(2022)` [[PDF]](https://arxiv.org/pdf/2206.09363.pdf) [[Code]](https://github.com/RUCAIBox/UniCRS) 157 | 31. **EGCR**: "EGCR: Explanation Generation for Conversational Recommendation." `arXiv(2022)` [[PDF]](https://arxiv.org/pdf/2208.08035.pdf) 158 | 32. "Improving Conversational Recommender System via Contextual and Time-Aware Modeling with Less Domain-Specific Knowledge." `arXiv(2022)` [[PDF]](https://arxiv.org/pdf/2209.11386.pdf) 159 | 33. **DICR**: "Aligning Recommendation and Conversation via Dual Imitation." `arXiv(2022)` [[PDF]](https://arxiv.org/pdf/2211.02848.pdf) 160 | 161 | 162 | ### Others 163 | 164 | 1. **Converse-Et-Impera**: "Converse-Et-Impera: Exploiting Deep Learning and Hierarchical Reinforcement Learning for Conversational Recommender Systems". `AI*IA(2017)` [[PDF]](https://www.researchgate.net/profile/Alessandro-Suglia/publication/320875588_Converse-Et-Impera_Exploiting_Deep_Learning_and_Hierarchical_Reinforcement_Learning_for_Conversational_Recommender_Systems/links/5bf6ad1592851c6b27d27324/Converse-Et-Impera-Exploiting-Deep-Learning-and-Hierarchical-Reinforcement-Learning-for-Conversational-Recommender-Systems.pdf) 165 | 166 | 2. "A Model of Social Explanations for a Conversational Movie Recommendation System". `HAI(2019)` [[PDF]](https://eprints.gla.ac.uk/193937/7/193937.pdf) 167 | 3. "Dynamic Online Conversation Recommendation". `ACL(2020)` [[PDF]](https://www.aclweb.org/anthology/2020.acl-main.305.pdf) [[Code]](https://github.com/zxshamson/dy-conv-rec) 168 | 4. **IAI MovieBot**: "IAI MovieBot: A Conversational Movie Recommender System". `CIKM(2020)` [[PDF]](https://arxiv.org/pdf/2009.03668.pdf) [[Code]](https://github.com/iai-group/moviebot) 169 | 5. **ConUCB**: "Conversational Contextual Bandit: Algorithm and Application". `WWW(2020)` [[PDF]](https://arxiv.org/pdf/1906.01219.pdf) [[Code]](https://github.com/Xiaoyinggit/ConUCB) 170 | 6. **Cora**: "A Socially-Aware Conversational Recommender System for Personalized Recipe Recommendations". `HAI(2020)` [[PDF]](https://www.researchgate.net/profile/Florian-Pecune/publication/346716927_A_Socially-Aware_Conversational_Recommender_System_for_Personalized_Recipe_Recommendations/links/5fcf621045851568d149d95e/A-Socially-Aware-Conversational-Recommender-System-for-Personalized-Recipe-Recommendations.pdf) 171 | 7. "Conversational Music Recommendation based on Bandits". `ICKG(2020)` [[Link]](https://ieeexplore.ieee.org/abstract/document/9194509/) 172 | 8. **n-by-p**: "Navigation-by-preference: a new conversational recommender with preference-based feedback". `IUI(2020)` [[PDF]](http://www.cs.ucc.ie/~dgb/papers/Rana-Bridge-2020.pdf) 173 | 9. "A Bayesian Approach to Conversational Recommendation Systems". `AAAI Workshop(2020)` [[PDF]](https://arxiv.org/pdf/2002.05063.pdf) 174 | 10. "Towards Retrieval-based Conversational Recommendation". `arXiv(2021)` [[PDF]](https://arxiv.org/pdf/2109.02311.pdf) 175 | 11. ""It doesn’t look good for a date": Transforming Critiques into Preferences for Conversational Recommendation Systems". `EMNLP(2021)` [[PDF]](https://arxiv.org/pdf/2109.07576.pdf) 176 | 177 | 178 | 179 | ## Other 180 | 181 | 1. **CCPE**: "Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences". `SIGDial(2019)` [[PDF]](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/54521b4011d0c2a19eaade8005ff4a499f754301.pdf) [[Dataset]](https://github.com/google-research-datasets/ccpe) 182 | 2. "Leveraging Historical Interaction Data for Improving Conversational Recommender System". `CIKM(2020)` [[PDF]](https://arxiv.org/pdf/2008.08247.pdf) [[Code]](https://github.com/Lancelot39/Pre-CRS) 183 | 3. "Evaluating Conversational Recommender Systems via User Simulation". `KDD(2020)` [[PDF]](https://arxiv.org/pdf/2006.08732.pdf) [[Code]](https://github.com/iai-group/UserSimConvRec) 184 | 4. "End-to-End Learning for Conversational Recommendation: A Long Way to Go?". `RecSys(2020)` [[PDF]](http://ceur-ws.org/Vol-2682/short1.pdf) [[Material]](https://drive.google.com/drive/folders/10gPOmaiFrZjIULIa3LsdmuyvJvnCV_Xq) 185 | 5. "What Does BERT Know about Books, Movies and Music? Probing BERT for Conversational Recommendation". `RecSys(2020)` [[PDF]](https://arxiv.org/pdf/2007.15356.pdf) [[Code]](https://github.com/Guzpenha/ConvRecProbingBERT) 186 | 6. "Latent Linear Critiquing for Conversational Recommender Systems". `WWW(2020)` [[PDF]](http://www.inago.com/wp-content/uploads/2020/08/UofT-Sanner_www20_llc.pdf) [[Code]](https://github.com/k9luo/LatentLinearCritiquingforConvRecSys) 187 | 7. "A Ranking Optimization Approach to Latent Linear Critiquing for Conversational Recommender Systems". `RecSys(2020)` [[Link]](https://dl.acm.org/doi/abs/10.1145/3383313.3412240) [[Code]](https://github.com/litosly/RankingOptimizationApproachtoLLC) 188 | 8. "A Comparison of Explicit and Implicit Proactive Dialogue Strategies for Conversational Recommendation". `LREC(2020)` [[PDF]](https://www.aclweb.org/anthology/2020.lrec-1.54.pdf) 189 | 9. "Predicting User Intents and Satisfaction with Dialogue-based Conversational Recommendations". `UMAP(2020)` [[PDF]](http://www.comp.hkbu.edu.hk/~lichen/download/Cai_UMAP20.pdf) [[Dataset]](https://wanlingcai.github.io/files/2020/UMAP2020_dataset_readme.html) 190 | 10. **ConveRSE**: "Conversational Recommender Systems and natural language: A study through the ConveRSE framework". `Decision Support Systems(2020)` [[Link]](https://www.sciencedirect.com/science/article/pii/S0167923620300051) [[Dataset]](https://github.com/swapUniba/ConvRecSysDataset) 191 | 192 | 11. "On Estimating the Training Cost of Conversational Recommendation Systems". `arXiv(2020)` [[PDF]](https://arxiv.org/pdf/2011.05302.pdf) 193 | 194 | 195 | 196 | ## Thesis 197 | 198 | 1. "Recommendation in Dialogue Systems". By [Yueming Sun](https://scholar.google.com/citations?user=UOYpBu4AAAAJ)(2019). [[PDF]](https://escholarship.org/content/qt4rs1s3ms/qt4rs1s3ms.pdf) 199 | 200 | 2. "Advanced Method Towards Conversational Recommendation". By [Yisong Miao](https://yisong.me/)(2020). [[PDF]](https://yisong.me/publications/Yisong_master_thesis-final.pdf) 201 | --------------------------------------------------------------------------------