{"id":31131054,"url":"https://github.com/zilize/crspapers","last_synced_at":"2026-02-13T20:03:07.063Z","repository":{"id":46710181,"uuid":"341907983","full_name":"Zilize/CRSPapers","owner":"Zilize","description":"Conversational Recommender System (CRS) paper list. 对话推荐系统论文列表","archived":false,"fork":false,"pushed_at":"2022-11-24T06:40:55.000Z","size":175,"stargazers_count":83,"open_issues_count":0,"forks_count":24,"subscribers_count":3,"default_branch":"main","last_synced_at":"2023-03-09T05:31:50.372Z","etag":null,"topics":["conversational-recommendation","deep-learning","dialogue-systems","paper-list","recommender-system"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Zilize.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2021-02-24T13:22:57.000Z","updated_at":"2023-03-08T07:41:17.000Z","dependencies_parsed_at":"2023-01-21T20:42:30.045Z","dependency_job_id":null,"html_url":"https://github.com/Zilize/CRSPapers","commit_stats":null,"previous_names":[],"tags_count":null,"template":null,"template_full_name":null,"purl":"pkg:github/Zilize/CRSPapers","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Zilize%2FCRSPapers","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Zilize%2FCRSPapers/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Zilize%2FCRSPapers/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Zilize%2FCRSPapers/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Zilize","download_url":"https://codeload.github.com/Zilize/CRSPapers/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Zilize%2FCRSPapers/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":275705498,"owners_count":25513170,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-09-18T02:00:09.552Z","response_time":77,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["conversational-recommendation","deep-learning","dialogue-systems","paper-list","recommender-system"],"created_at":"2025-09-18T03:50:26.233Z","updated_at":"2025-09-18T03:50:27.930Z","avatar_url":"https://github.com/Zilize.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# CRS Papers\n\n![](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)\n\nA Conversational Recommender System (CRS) is defined by [Gao et al. (2021)](https://arxiv.org/pdf/2101.09459.pdf) as following:\n\n\u003e *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.*\n\n### Contents\n\n- [Quick-Start](#Quick-Start)\n- [Survey and Tutorial](#Survey-and-Tutorial)\n  - [Survey](#Survey)\n  - [Tutorial](#Tutorial)\n- [Toolkit and Dataset](#Toolkit-and-Dataset)\n  - [Toolkit](#Toolkit)\n  - [Dataset](#Dataset)\n- [Model](#Model)\n  - [Attribute-based](#Attribute-based)\n  - [Generation-based](#Generation-based)\n  - [Others](#Others)\n- [Other](#Other)\n- [Thesis](#Thesis)\n\n\n\n## Quick-Start\n\n\u003e A quick-start paper list including survey, tutorial, toolkit and model papers.\n\n1. \"Deep Conversational Recommender Systems: A New Frontier for Goal-Oriented Dialogue Systems\". `arXiv(2020)` [[PDF]](https://arxiv.org/pdf/2004.13245.pdf)\n2. \"Tutorial on Conversational Recommendation Systems\". `RecSys(2020)` [[PDF]](http://yongfeng.me/attach/fu-recsys2020.pdf) [[Homepage]](https://conversational-recsys.github.io/)\n3. **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)\n4. **CRM**: \"Conversational Recommender System\". `SIGIR(2018)` [[PDF]](https://arxiv.org/pdf/1806.03277) [[Homepage]](https://github.com/ysun30/ConvRec)\n5. **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)\n6. **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/)\n7. **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/)\n8. **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)\n9. **KBRD**: \"Towards Knowledge-Based Recommender Dialog System\". `EMNLP-IJCNLP(2019)` [[PDF]](https://arxiv.org/pdf/1908.05391.pdf) [[Code]](https://github.com/THUDM/KBRD)\n\n10. **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)\n\n\n\n## Survey and Tutorial\n\n### Survey\n\n1. \"Deep Conversational Recommender Systems: A New Frontier for Goal-Oriented Dialogue Systems\". `arXiv(2020)` [[PDF]](https://arxiv.org/pdf/2004.13245.pdf)\n2. \"A survey on conversational recommender systems\". `arXiv(2020)` [[PDF]](https://arxiv.org/pdf/2004.00646.pdf)\n\n3. \"Advances and Challenges in Conversational Recommender Systems: A Survey\". `arXiv(2021)` [[PDF]](https://arxiv.org/pdf/2101.09459.pdf)\n\n### Tutorial\n\n1. \"Tutorial on Conversational Recommendation Systems\". [[Homepage]](https://conversational-recsys.github.io/)\n   - `RecSys(2020)` [[PDF]](http://yongfeng.me/attach/fu-recsys2020.pdf)\n   - `WSDM(2021)` [[PDF]](http://yongfeng.me/attach/fu-wsdm2021.pdf)\n   - `IUI(2021)` [[PDF]](http://yongfeng.me/attach/fu-iui2021.pdf)\n\n2. \"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)\n\n\n\n## Toolkit and Dataset\n\n### Toolkit\n\n1. **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)\n\n### Dataset\n\n1. **ConvRec**: \"Conversational Recommender System\". `SIGIR(2018)` [[PDF]](https://arxiv.org/pdf/1806.03277) [[Homepage]](https://github.com/ysun30/ConvRec)\n\n2. **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)\n3. **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/)\n4. **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/)\n5. **ReDial**: \"Towards Deep Conversational Recommendations\". `NeurIPS(2018)` [[PDF]](https://arxiv.org/pdf/1812.07617) [[Homepage]](https://redialdata.github.io/website/)\n6. **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)\n7. **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)\n8. **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)\n9. **TG-ReDial**: \"Towards Topic-Guided Conversational Recommender System\". `COLING(2020)` [[PDF]](https://arxiv.org/pdf/2010.04125) [[Homepage]](https://github.com/RUCAIBox/TG-ReDial)\n10. **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)\n11. **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)\n12. **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)\n13. **MGConvRex**: \"User Memory Reasoning for Conversational Recommendation\". `ACL(2020)` [[PDF]](https://arxiv.org/pdf/2006.00184)\n14. **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)\n15. **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)\n16. **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)\n17. **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)\n18. **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)\n\n\n## Model\n\n### Attribute-based\n\n\u003e Attribute-based CRSs typically capture user preferences by asking queries about item attributes and generates responses using pre-defined templates.\n\n1. \"Towards Conversational Recommender Systems\". `KDD(2016)` [[PDF]](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/rfp0063-christakopoulou.pdf)\n2. **CRM**: \"Conversational Recommender System\". `SIGIR(2018)` [[PDF]](https://arxiv.org/pdf/1806.03277) [[Homepage]](https://github.com/ysun30/ConvRec)\n3. **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)\n4. **Q\u0026R**: \"Q\u0026R: A Two-Stage Approach toward Interactive Recommendation\". `KDD(2018)` [[PDF]](http://www.alexbeutel.com/papers/q-and-r-kdd2018.pdf)\n5. \"Dialogue based recommender system that flexibly mixes utterances and recommendations\". `WI(2019)` [[Link]](https://ieeexplore.ieee.org/abstract/document/8909617)\n6. **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/)\n7. **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/)\n8. **CRSAL**: \"CRSAL: Conversational Recommender Systems with Adversarial Learning\". `TOIS(2020)` [[PDF]](https://repository.kaust.edu.sa/bitstream/handle/10754/665725/TOIS.pdf?sequence=1\u0026isAllowed=y) [[Code]](https://github.com/XuhuiRen/CRSAL)\n9. **Qrec**: \"Towards Question-Based Recommender Systems\". `SIGIR(2020)` [[PDF]](https://arxiv.org/pdf/2005.14255.pdf) [[Code]](https://github.com/JieZouIR/Qrec)\n10. **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)\n11. **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)\n12. **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)\n13. **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)\n\n14. \"Developing a Conversational Recommendation System for Navigating Limited Options\". `CHI(2021)` [[PDF]](https://arxiv.org/pdf/2104.06552.pdf)\n15. **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)\n16. \"Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems.\" `CIKM(2022)` [[PDF]](https://arxiv.org/pdf/2208.03298.pdf)\n17. **MINICORN**: \"Minimalist and High-performance Conversational Recommendation with Uncertainty Estimation for User Preference.\" `arXiv(2022)` [[PDF]](https://arxiv.org/pdf/2206.14468.pdf)\n18. **CRIF**: \"Learning to Infer User Implicit Preference in Conversational Recommendation.\" `SIGIR(2022)` [[PDF]](https://dl.acm.org/doi/abs/10.1145/3477495.3531844)\n19. **HICR**: \"Conversational Recommendation via Hierarchical Information Modeling.\" `SIGIR(2022)` [[PDF]](https://dl.acm.org/doi/abs/10.1145/3477495.3531830)\n20. **MetaCRS**: \"Meta Policy Learning for Cold-Start Conversational Recommendation.\" `WSDM(2023)` [[PDF]](https://arxiv.org/pdf/2205.11788.pdf)\n\n### Generation-based\n\n\u003e Compared to attribute-based CRSs, generation-based CRSs pay more attention to generate human-like responses in natural language.\n\n1. **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/)\n\n2. **KBRD**: \"Towards Knowledge-Based Recommender Dialog System\". `EMNLP-IJCNLP(2019)` [[PDF]](https://arxiv.org/pdf/1908.05391.pdf) [[Code]](https://github.com/THUDM/KBRD)\n3. **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)\n4. **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)\n5. **DCR**: \"Deep Conversational Recommender in Travel\". `TKDE(2020)` [[PDF]](https://arxiv.org/pdf/1907.00710.pdf) [[Code]](https://github.com/truthless11/DCR)\n6. **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)\n7. **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)\n8. **ECR**: \"Towards Explainable Conversational Recommendation\". `IJCAI(2020)` [[PDF]](https://www.ijcai.org/Proceedings/2020/0414.pdf)\n9. **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)\n10. **TG-ReDial**: \"Towards Topic-Guided Conversational Recommender System\". `COLING(2020)` [[PDF]](https://arxiv.org/pdf/2010.04125) [[Homepage]](https://github.com/RUCAIBox/TG-ReDial)\n11. **MGConvRex**: \"User Memory Reasoning for Conversational Recommendation\". `COLING(2020)` [[PDF]](https://arxiv.org/pdf/2006.00184)\n12. **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)\n13. **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)\n14. **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)\n15. **KECRS**: \"KECRS: Towards Knowledge-Enriched Conversational Recommendation System\". `arXiv(2021)` [[PDF]](https://arxiv.org/pdf/2105.08261.pdf)\n16. \"Category Aware Explainable Conversational Recommendation\". `arXiv(2021)` [[PDF]](https://arxiv.org/pdf/2103.08733.pdf)\n17. **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)\n18. **NTRD**: \"Learning Neural Templates for Recommender Dialogue System.\" `EMNLP(2021)` [[PDF]](https://arxiv.org/pdf/2109.12302.pdf) [[Code]](https://github.com/jokieleung/NTRD)\n19. **CRFR**: \"CRFR: Improving Conversational Recommender Systems via Flexible Fragments Reasoning on Knowledge Graphs.\" `EMNLP(2021)` [[PDF]](https://aclanthology.org/2021.emnlp-main.355.pdf)\n20. **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)\n21. **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)\n22. **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)\n23. **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)\n24. **BARCOR**: \"BARCOR: Towards A Unified Framework for Conversational Recommendation Systems.\" `arXiv(2022)` [[PDF]](https://arxiv.org/pdf/2203.14257.pdf)\n25. **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)\n26. **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)\n27. **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)\n28. **TSCR**: \"Improving Conversational Recommender Systems via Transformer-based Sequential Modelling.\" `SIGIR(2022)` [[PDF]](https://dl.acm.org/doi/abs/10.1145/3477495.3531852)\n29. **CCRS**: \"Customized Conversational Recommender Systems.\" `ECML-PKDD(2022)` [[PDF]](https://arxiv.org/pdf/2207.00814.pdf)\n30. **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)\n31. **EGCR**: \"EGCR: Explanation Generation for Conversational Recommendation.\" `arXiv(2022)` [[PDF]](https://arxiv.org/pdf/2208.08035.pdf)\n32. \"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)\n33. **DICR**: \"Aligning Recommendation and Conversation via Dual Imitation.\" `arXiv(2022)` [[PDF]](https://arxiv.org/pdf/2211.02848.pdf)\n\n\n### Others\n\n1. **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)\n\n2. \"A Model of Social Explanations for a Conversational Movie Recommendation System\". `HAI(2019)` [[PDF]](https://eprints.gla.ac.uk/193937/7/193937.pdf)\n3. \"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)\n4. **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)\n5. **ConUCB**: \"Conversational Contextual Bandit: Algorithm and Application\". `WWW(2020)` [[PDF]](https://arxiv.org/pdf/1906.01219.pdf) [[Code]](https://github.com/Xiaoyinggit/ConUCB)\n6. **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)\n7. \"Conversational Music Recommendation based on Bandits\". `ICKG(2020)` [[Link]](https://ieeexplore.ieee.org/abstract/document/9194509/)\n8. **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)\n9. \"A Bayesian Approach to Conversational Recommendation Systems\". `AAAI Workshop(2020)` [[PDF]](https://arxiv.org/pdf/2002.05063.pdf)\n10. \"Towards Retrieval-based Conversational Recommendation\". `arXiv(2021)` [[PDF]](https://arxiv.org/pdf/2109.02311.pdf)\n11. \"\"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)\n\n\n\n## Other\n\n1. **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)\n2. \"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)\n3. \"Evaluating Conversational Recommender Systems via User Simulation\". `KDD(2020)` [[PDF]](https://arxiv.org/pdf/2006.08732.pdf) [[Code]](https://github.com/iai-group/UserSimConvRec)\n4. \"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)\n5. \"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)\n6. \"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)\n7. \"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)\n8. \"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)\n9. \"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)\n10. **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)\n\n11. \"On Estimating the Training Cost of Conversational Recommendation Systems\". `arXiv(2020)` [[PDF]](https://arxiv.org/pdf/2011.05302.pdf)\n\n\n\n## Thesis\n\n1. \"Recommendation in Dialogue Systems\". By [Yueming Sun](https://scholar.google.com/citations?user=UOYpBu4AAAAJ)(2019). [[PDF]](https://escholarship.org/content/qt4rs1s3ms/qt4rs1s3ms.pdf)\n\n2. \"Advanced Method Towards Conversational Recommendation\". By [Yisong Miao](https://yisong.me/)(2020). [[PDF]](https://yisong.me/publications/Yisong_master_thesis-final.pdf)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzilize%2Fcrspapers","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fzilize%2Fcrspapers","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzilize%2Fcrspapers/lists"}