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
• Survey and Tutorial
  • Survey
  • Tutorial
• Toolkit and Dataset
  • Toolkit
  • Dataset
• Model
  • Attribute-based
  • Generation-based
  • Others
• Other
• Thesis

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". ACL(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". [Homepage]

   • RecSys(2020) [PDF]
   • WSDM(2021) [PDF]
   • IUI(2021) [PDF]

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". ACL(2021) [PDF] [Homepage]

Dataset

1. ConvRec: "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]

5. ReDial: "Towards Deep Conversational Recommendations". NeurIPS(2018) [PDF] [Homepage]

6. OpenDialKG: "OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs". ACL(2019) [PDF] [Homepage]

7. PersuasionForGood: "Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good". ACL(2019) [PDF] [Homepage]

8. CCPE: "Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences". SIGDial(2019) [PDF] [Homepage]

9. TG-ReDial: "Towards Topic-Guided Conversational Recommender System". COLING(2020) [PDF] [Homepage]

10. GoRecDial: "Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue". EMNLP(2019) [PDF] [Download]

11. DuRecDial: "Towards Conversational Recommendation over Multi-Type Dialogs". ACL(2020) [PDF] [Download]

12. INSPIRED: "INSPIRED: Toward Sociable Recommendation Dialogue Systems". EMNLP(2020) [PDF] [Homepage]

13. MGConvRex: "User Memory Reasoning for Conversational Recommendation". ACL(2020) [PDF]

14. COOKIE: "COOKIE: A Dataset for Conversational Recommendation over Knowledge Graphs in E-commerce". arXiv(2020) [PDF] [Homepage]

15. IARD: "Predicting User Intents and Satisfaction with Dialogue-based Conversational Recommendations". UMAP(2020) [PDF] [Homepage]

16. DuRecDial 2.0: "DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation". EMNLP(2021) [PDF] [Homepage]

17. MMConv: "MMConv: An Environment for Multimodal Conversational Search across Multiple Domains". SIGIR(2021) [PDF] [Homepage]

18. INSPIRED2: "INSPIRED2: An Improved Dataset for Sociable Conversational Recommendation." RecSys(2022) [PDF] [Homepage]

Model

Attribute-based

Attribute-based CRSs typically capture user preferences by asking queries about item attributes and generates responses using pre-defined templates.

1. "Towards Conversational Recommender Systems". KDD(2016) [PDF]

2. CRM: "Conversational Recommender System". SIGIR(2018) [PDF] [Homepage]

3. SAUR: "Towards Conversational Search and Recommendation: System Ask, User Respond". CIKM(2018) [PDF] [Dataset]

4. Q&R: "Q&R: A Two-Stage Approach toward Interactive Recommendation". KDD(2018) [PDF]

5. "Dialogue based recommender system that flexibly mixes utterances and recommendations". WI(2019) [Link]

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. CRSAL: "CRSAL: Conversational Recommender Systems with Adversarial Learning". TOIS(2020) [PDF] [Code]

9. Qrec: "Towards Question-Based Recommender Systems". SIGIR(2020) [PDF] [Code]

10. ConTS: "Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users". TOIS(2021) [PDF] [Code]

11. UNICORN: "Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning". SIGIR(2021) [PDF] [Code]

12. KBQG: "Learning to Ask Appropriate Questions in Conversational Recommendation". arXiv(2021) [PDF] [Code]

13. FPAN: "Adapting User Preference to Online Feedback in Multi-round Conversational Recommendation". WSDM(2021) [Link] [Code]

14. "Developing a Conversational Recommendation System for Navigating Limited Options". CHI(2021) [PDF]

15. MCMIPL: "Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation." WWW(2022) [PDF] [Code]

16. "Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems." CIKM(2022) [PDF]

17. MINICORN: "Minimalist and High-performance Conversational Recommendation with Uncertainty Estimation for User Preference." arXiv(2022) [PDF]

18. CRIF: "Learning to Infer User Implicit Preference in Conversational Recommendation." SIGIR(2022) [PDF]

19. HICR: "Conversational Recommendation via Hierarchical Information Modeling." SIGIR(2022) [PDF]

20. MetaCRS: "Meta Policy Learning for Cold-Start Conversational Recommendation." WSDM(2023) [PDF]

Generation-based

Compared to attribute-based CRSs, generation-based CRSs pay more attention to generate human-like responses in natural language.

1. ReDial: "Towards Deep Conversational Recommendations". NeurIPS(2018) [PDF] [Code] [Dataset]

2. KBRD: "Towards Knowledge-Based Recommender Dialog System". EMNLP-IJCNLP(2019) [PDF] [Code]

3. GoRecDial: "Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue". EMNLP(2019) [PDF] [Code] [Dataset]

4. DialKG Walker: "OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs". ACL(2019) [PDF] [Code] [Dataset]

5. DCR: "Deep Conversational Recommender in Travel". TKDE(2020) [PDF] [Code]

6. KGSF: "Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion". KDD(2020) [PDF] [Code]

7. MGCG: "Towards Conversational Recommendation over Multi-Type Dialogs". ACL(2020) [PDF] [Code] [Dataset]

8. ECR: "Towards Explainable Conversational Recommendation". IJCAI(2020) [PDF]

9. INSPIRED: "INSPIRED: Toward Sociable Recommendation Dialogue Systems". EMNLP(2020) [PDF] [Homepage]

10. TG-ReDial: "Towards Topic-Guided Conversational Recommender System". COLING(2020) [PDF] [Homepage]

11. MGConvRex: "User Memory Reasoning for Conversational Recommendation". COLING(2020) [PDF]

12. KGConvRec: "Suggest me a movie for tonight: Leveraging Knowledge Graphs for Conversational Recommendation". COLING(2020) [PDF] [Code]

13. CR-Walker: "Bridging the Gap between Conversational Reasoning and Interactive Recommendation". arXiv(2020) [PDF] [Code]

14. RevCore: "RevCore: Review-augmented Conversational Recommendation". ACL-Findings(2021) [PDF] [Code]

15. KECRS: "KECRS: Towards Knowledge-Enriched Conversational Recommendation System". arXiv(2021) [PDF]

16. "Category Aware Explainable Conversational Recommendation". arXiv(2021) [PDF]

17. DuRecDial 2.0: "DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation". EMNLP(2021) [PDF] [Dataset]

18. NTRD: "Learning Neural Templates for Recommender Dialogue System." EMNLP(2021) [PDF] [Code]

19. CRFR: "CRFR: Improving Conversational Recommender Systems via Flexible Fragments Reasoning on Knowledge Graphs." EMNLP(2021) [PDF]

20. RID: "Finetuning Large-Scale Pre-trained Language Models for Conversational Recommendation with Knowledge Graph." arXiv(2021) [PDF] [Code]

21. RecInDial: "RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models." AACL(2022) [PDF] [Code]

22. MESE: "Improving Conversational Recommendation Systems’ Quality with Context-Aware Item Meta Information." NAACL(2022) [PDF] [Code]

23. C2-CRS: "C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System." WSDM(2022) [PDF] [Code]

24. BARCOR: "BARCOR: Towards A Unified Framework for Conversational Recommendation Systems." arXiv(2022) [PDF]

25. UniMIND: "A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems." TOIS(2023) [PDF] [Code]

26. UCCR: "User-Centric Conversational Recommendation with Multi-Aspect User Modeling." SIGIR(2022) [PDF] [Code]

27. UPCR: "Variational Reasoning about User Preferences for Conversational Recommendation." SIGIR(2022) [PDF] [Code]

28. TSCR: "Improving Conversational Recommender Systems via Transformer-based Sequential Modelling." SIGIR(2022) [PDF]

29. CCRS: "Customized Conversational Recommender Systems." ECML-PKDD(2022) [PDF]

30. UniCRS: "Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning." KDD(2022) [PDF] [Code]

31. EGCR: "EGCR: Explanation Generation for Conversational Recommendation." arXiv(2022) [PDF]

32. "Improving Conversational Recommender System via Contextual and Time-Aware Modeling with Less Domain-Specific Knowledge." arXiv(2022) [PDF]

33. DICR: "Aligning Recommendation and Conversation via Dual Imitation." arXiv(2022) [PDF]

Others

1. Converse-Et-Impera: "Converse-Et-Impera: Exploiting Deep Learning and Hierarchical Reinforcement Learning for Conversational Recommender Systems". AI*IA(2017) [PDF]

2. "A Model of Social Explanations for a Conversational Movie Recommendation System". HAI(2019) [PDF]

3. "Dynamic Online Conversation Recommendation". ACL(2020) [PDF] [Code]

4. IAI MovieBot: "IAI MovieBot: A Conversational Movie Recommender System". CIKM(2020) [PDF] [Code]

5. ConUCB: "Conversational Contextual Bandit: Algorithm and Application". WWW(2020) [PDF] [Code]

6. Cora: "A Socially-Aware Conversational Recommender System for Personalized Recipe Recommendations". HAI(2020) [PDF]

7. "Conversational Music Recommendation based on Bandits". ICKG(2020) [Link]

8. n-by-p: "Navigation-by-preference: a new conversational recommender with preference-based feedback". IUI(2020) [PDF]

9. "A Bayesian Approach to Conversational Recommendation Systems". AAAI Workshop(2020) [PDF]

10. "Towards Retrieval-based Conversational Recommendation". arXiv(2021) [PDF]

11. ""It doesn’t look good for a date": Transforming Critiques into Preferences for Conversational Recommendation Systems". EMNLP(2021) [PDF]

Other

1. CCPE: "Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences". SIGDial(2019) [PDF] [Dataset]

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. "A Ranking Optimization Approach to Latent Linear Critiquing for Conversational Recommender Systems". RecSys(2020) [Link] [Code]

8. "A Comparison of Explicit and Implicit Proactive Dialogue Strategies for Conversational Recommendation". LREC(2020) [PDF]

9. "Predicting User Intents and Satisfaction with Dialogue-based Conversational Recommendations". UMAP(2020) [PDF] [Dataset]

10. ConveRSE: "Conversational Recommender Systems and natural language: A study through the ConveRSE framework". Decision Support Systems(2020) [Link] [Dataset]

11. "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]