Skip to content

Latest commit

 

History

History
200 lines (148 loc) · 23.5 KB

README.md

File metadata and controls

200 lines (148 loc) · 23.5 KB
Raw

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". 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]

  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]