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Conversational Recommender System (CRS) paper list. 对话推荐系统论文列表

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CRS Papers

A Conversational Recommender System (CRS) is defined by Gao et al. (2021) as following:

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.

Contents

Quick-Start

A quick-start paper list including survey, tutorial, toolkit and model papers.

  1. "Deep Conversational Recommender Systems: A New Frontier for Goal-Oriented Dialogue Systems". arXiv(2020) [PDF]
  2. "Tutorial on Conversational Recommendation Systems". RecSys(2020) [PDF] [Homepage]
  3. CRSLab: "CRSLab: An Open-Source Toolkit for Building Conversational Recommender System". arXiv(2021) [PDF] [Homepage]
  4. CRM: "Conversational Recommender System". SIGIR(2018) [PDF] [Homepage]
  5. SAUR: "Towards Conversational Search and Recommendation: System Ask, User Respond". CIKM(2018) [PDF] [Dataset]
  6. EAR: "Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems". WSDM(2020) [PDF] [Homepage]
  7. CPR: "Interactive Path Reasoning on Graph for Conversational Recommendation". KDD(2020) [PDF] [Homepage]
  8. ReDial: "Towards Deep Conversational Recommendations". NeurIPS(2018) [PDF] [Dataset] [Code]
  9. KBRD: "Towards Knowledge-Based Recommender Dialog System". EMNLP-IJCNLP(2019) [PDF] [Code]
  10. KGSF: "Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion". KDD(2020) [PDF] [Code]

Survey and Tutorial

Survey

  1. "Deep Conversational Recommender Systems: A New Frontier for Goal-Oriented Dialogue Systems". arXiv(2020) [PDF]
  2. "A survey on conversational recommender systems". arXiv(2020) [PDF]
  3. "Advances and Challenges in Conversational Recommender Systems: A Survey". arXiv(2021) [PDF]

Tutorial

  1. "Tutorial on Conversational Recommendation Systems". RecSys(2020) [PDF] [Homepage]
  2. "Conversational Recommendation: Formulation, Methods, and Evaluation". SIGIR(2020) [PDF] [Slides]

Toolkit and Dataset

Toolkit

  1. CRSLab: "CRSLab: An Open-Source Toolkit for Building Conversational Recommender System". arXiv(2021) [PDF] [Homepage]

Dataset

The following datasets are used for template-based CRS.

  1. CRM: "Conversational Recommender System". SIGIR(2018) [PDF] [Homepage]
  2. SAUR: "Towards Conversational Search and Recommendation: System Ask, User Respond". CIKM(2018) [PDF] [Download]
  3. EAR: "Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems". WSDM(2020) [PDF] [Homepage]
  4. CPR: "Interactive Path Reasoning on Graph for Conversational Recommendation". KDD(2020) [PDF] [Homepage]

The following datasets are used for end-to-end CRS.

  1. ReDial: "Towards Deep Conversational Recommendations". NeurIPS(2018) [PDF] [Homepage]
  2. OpenDialKG: "OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs". ACL(2019) [PDF] [Homepage]
  3. PersuasionForGood: "Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good". ACL(2019) [PDF] [Homepage]
  4. CCPE: "Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences". SIGDial(2019) [PDF] [Homepage]
  5. TG-ReDial: "Towards Topic-Guided Conversational Recommender System". COLING(2020) [PDF] [Homepage]
  6. GoRecDial: "Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue". EMNLP(2019) [PDF] [Download]
  7. DuRecDial: "Towards Conversational Recommendation over Multi-Type Dialogs". ACL(2020) [PDF] [Download]
  8. INSPIRED: "INSPIRED: Toward Sociable Recommendation Dialogue Systems". EMNLP(2020) [PDF] [Homepage]
  9. MGConvRex: "User Memory Reasoning for Conversational Recommendation". ACL(2020) [PDF]
  10. COOKIE: "COOKIE: A Dataset for Conversational Recommendation over Knowledge Graphs in E-commerce". arXiv(2020) [PDF] [Homepage]
  11. IARD: "Predicting User Intents and Satisfaction with Dialogue-based Conversational Recommendations". UMAP(2020) [PDF] [Homepage]

Model

  1. "Towards Conversational Recommender Systems". KDD(2016) [PDF]
  2. Converse-Et-Impera: "Converse-Et-Impera: Exploiting Deep Learning and Hierarchical Reinforcement Learning for Conversational Recommender Systems". AI*IA(2017) [PDF]
  3. CRM: "Conversational Recommender System". SIGIR(2018) [PDF] [Homepage]
  4. ReDial: "Towards Deep Conversational Recommendations". NeurIPS(2018) [PDF] [Code] [Dataset: ReDial]
  5. SAUR: "Towards Conversational Search and Recommendation: System Ask, User Respond". CIKM(2018) [PDF] [Dataset: SAUR]
  6. Q&R: "Q&R: A Two-Stage Approach toward Interactive Recommendation". KDD(2018) [PDF]
  7. KBRD: "Towards Knowledge-Based Recommender Dialog System". EMNLP-IJCNLP(2019) [PDF] [Code]
  8. GoRecDial: "Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue". EMNLP(2019) [PDF] [Code] [Dataset: GoRecDial]
  9. DialKG Walker: "OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs". ACL(2019) [PDF] [Code] [Dataset: OpenDialKG]
  10. "Dialogue based recommender system that flexibly mixes utterances and recommendations". WI(2019) [Link]
  11. "A Model of Social Explanations for a Conversational Movie Recommendation System". HAI(2019) [PDF]
  12. DCR: "Deep Conversational Recommender in Travel". TKDE(2020) [PDF] [Code]
  13. EAR: "Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems". WSDM(2020) [PDF] [Homepage]
  14. CPR: "Interactive Path Reasoning on Graph for Conversational Recommendation". KDD(2020) [PDF] [Homepage]
  15. KGSF: "Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion". KDD(2020) [PDF] [Code]
  16. MGCG: "Towards Conversational Recommendation over Multi-Type Dialogs". ACL(2020) [PDF] [Code] [Dataset: DuRecDial]
  17. "Dynamic Online Conversation Recommendation". ACL(2020) [PDF] [Code]
  18. ECR: "Towards Explainable Conversational Recommendation". IJCAI(2020) [PDF]
  19. INSPIRED: "INSPIRED: Toward Sociable Recommendation Dialogue Systems". EMNLP(2020) [PDF] [Homepage]
  20. TG-ReDial: "Towards Topic-Guided Conversational Recommender System". COLING(2020) [PDF] [Homepage]
  21. MGConvRex: "User Memory Reasoning for Conversational Recommendation". COLING(2020) [PDF]
  22. KGConvRec: "Suggest me a movie for tonight: Leveraging Knowledge Graphs for Conversational Recommendation". COLING(2020) [PDF] [Code]
  23. CRSAL: "CRSAL: Conversational Recommender Systems with Adversarial Learning". TOIS(2020) [PDF] [Code]
  24. CR-Walker: "Bridging the Gap between Conversational Reasoning and Interactive Recommendation". arXiv(2020) [PDF] [Code]
  25. Qrec: "Towards Question-Based Recommender Systems". SIGIR(2020) [PDF] [Code]
  26. IAI MovieBot: "IAI MovieBot: A Conversational Movie Recommender System". CIKM(2020) [PDF] [Code]
  27. ConUCB: "Conversational Contextual Bandit: Algorithm and Application". WWW(2020) [PDF] [Code]
  28. Cora: "A Socially-Aware Conversational Recommender System for Personalized Recipe Recommendations". HAI(2020) [PDF]
  29. "Conversational Music Recommendation based on Bandits". ICKG(2020) [Link]
  30. "A Bayesian Approach to Conversational Recommendation Systems". AAAI Workshop(2020) [PDF]
  31. ConTS: "Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users". TOIS(2021) [PDF] [Code]

Other

  1. CCPE: "Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences". SIGDial(2019) [PDF] [Dataset: CCPE]
  2. "Leveraging Historical Interaction Data for Improving Conversational Recommender System". CIKM(2020) [PDF] [Code]
  3. "Evaluating Conversational Recommender Systems via User Simulation". KDD(2020) [PDF] [Code]
  4. "End-to-End Learning for Conversational Recommendation: A Long Way to Go?". RecSys(2020) [PDF] [Material]
  5. "What Does BERT Know about Books, Movies and Music? Probing BERT for Conversational Recommendation". RecSys(2020) [PDF] [Code]
  6. "Latent Linear Critiquing for Conversational Recommender Systems". WWW(2020) [PDF] [Code]
  7. "Predicting User Intents and Satisfaction with Dialogue-based Conversational Recommendations". UMAP(2020) [PDF] [Dataset: IARD]
  8. ConveRSE: "Conversational Recommender Systems and natural language: A study through the ConveRSE framework". Decision Support Systems(2020) [Link] [Dataset: ConvRecSysDataset]
  9. "On Estimating the Training Cost of Conversational Recommendation Systems". arXiv(2020) [PDF]

Thesis

  1. "Recommendation in Dialogue Systems". By Yueming Sun(2019). [PDF]
  2. "Advanced Method Towards Conversational Recommendation". By Yisong Miao(2020). [PDF]

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Conversational Recommender System (CRS) paper list. 对话推荐系统论文列表

License:MIT License