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Towards Deep Conversational Recommendations

arXiv 1812.07617 ↗First posted 18 Dec 2018cs.LG cs.CL cs.IRcited by 174

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Abstract (arXiv)

There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational recommendation is an interesting setting for the scientific exploration of dialogue with natural language as the associated discourse involves goal-driven dialogue that often transforms naturally into more free-form chat. This paper provides two contributions. First, until now there has been no publicly available large-scale dataset consisting of real-world dialogues centered around recommendations. To address this issue and to facilitate our exploration here, we have collected ReDial, a dataset consisting of over 10,000 conversations centered around the theme of providing movie recommendations. We make this data available to the community for further research. Second, we use this dataset to explore multiple facets of conversational recommendations. In particular we explore new neural architectures, mechanisms, and methods suitable for composing conversational recommendation systems. Our dataset allows us to systematically probe model sub-components addressing different parts of the overall problem domain ranging from: sentiment analysis and cold-start recommendation generation to detailed aspects of how natural language is used in this setting in the real world. We combine such sub-components into a full-blown dialogue system and examine its behavior.

Key facts. Cited by 174 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Dec 2018.

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Citing papers per year: 2021: 23; 2022: 28; 2023: 41; 2024: 31; 2025: 28; 2026: 23.

174 papers.

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  1. 2025Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactionscited by 236
  2. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 141
  3. 2023Recommender AI Agent: Integrating Large Language Models for Interactive Recommendationscited by 130
  4. 2023Large Language Models as Zero-Shot Conversational Recommenderscited by 126
  5. 2021Advances and Challenges in Conversational Recommender Systems: A Surveycited by 118
  6. 2023When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunitiescited by 89
  7. 2022Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learningcited by 85
  8. 2022Personalized Prompt Learning for Explainable Recommendationcited by 79
  9. 2023A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospectscited by 66
  10. 2023Leveraging Large Language Models in Conversational Recommender Systemscited by 63
  11. 2024A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)cited by 58
  12. 2025A Survey on LLM-powered Agents for Recommender Systemscited by 49
38 more
  1. 2023Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Modelscited by 48
  2. 2021RevCore: Review-augmented Conversational Recommendationcited by 47
  3. 2021DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendationcited by 46
  4. 2024Evaluating Large Language Models as Generative User Simulators for Conversational Recommendationcited by 45
  5. 2022Neural Approaches to Conversational Information Retrievalcited by 41
  6. 2021CRSLab: An Open-Source Toolkit for Building Conversational Recommender Systemcited by 34
  7. 2021Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representationcited by 32
  8. 2022A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systemscited by 32
  9. 2022Meet Your Favorite Character: Open-domain Chatbot Mimicking Fictional Characters with only a Few Utterancescited by 32
  10. 2022Knowledge Is Flat: A Seq2Seq Generative Framework for Various Knowledge Graph Completioncited by 32
  11. 2023A Large Language Model Enhanced Conversational Recommender Systemcited by 31
  12. 2022User-Centric Conversational Recommendation with Multi-Aspect User Modelingcited by 29
  13. 2021Improving Conversational Recommendation Systems' Quality with Context-Aware Item Meta Informationcited by 26
  14. 2024How Reliable is Your Simulator? Analysis on the Limitations of Current LLM-based User Simulators for Conversational Recommendationcited by 25
  15. 2021Learning Neural Templates for Recommender Dialogue Systemcited by 22
  16. 2022Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendationcited by 22
  17. 2024A Survey of Conversational Searchcited by 22
  18. 2022BARCOR: Towards A Unified Framework for Conversational Recommendation Systemscited by 21
  19. 2023DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AIcited by 21
  20. 2024Self-Updatable Large Language Models by Integrating Context into Model Parameterscited by 21
  21. 2022User Satisfaction Estimation with Sequential Dialogue Act Modeling in Goal-oriented Conversational Systemscited by 20
  22. 2023Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankerscited by 19
  23. 2023TREA: Tree-Structure Reasoning Schema for Conversational Recommendationcited by 15
  24. 2023Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales Dialoguecited by 15
  25. 2024Pearl: A Review-driven Persona-Knowledge Grounded Conversational Recommendation Datasetcited by 15
  26. 2021KECRS: Towards Knowledge-Enriched Conversational Recommendation Systemcited by 14
  27. 2021RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Modelscited by 14
  28. 2022Social Influence Dialogue Systems: A Survey of Datasets and Models For Social Influence Taskscited by 14
  29. 2024Zero-shot Persuasive Chatbots with LLM-Generated Strategies and Information Retrievalcited by 14
  30. 2022Comparison-based Conversational Recommender System with Relative Bandit Feedbackcited by 12
  31. 2023U-NEED: A Fine-grained Dataset for User Needs-Centric E-commerce Conversational Recommendationcited by 12
  32. 2023The Sentiment Problem: A Critical Survey towards Deconstructing Sentiment Analysiscited by 12
  33. 2023Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data Augmentationcited by 11
  34. 2024Behavior Alignment: A New Perspective of Evaluating LLM-based Conversational Recommender Systemscited by 11
  35. 2024Elaborative Subtopic Query Reformulation for Broad and Indirect Queries in Travel Destination Recommendationcited by 11
  36. 2021Generating Usage-related Questions for Preference Elicitation in Conversational Recommender Systemscited by 10
  37. 2022Bundle MCR: Towards Conversational Bundle Recommendationcited by 10
  38. 2022Hierarchical Conversational Preference Elicitation with Bandit Feedbackcited by 10

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