mlsanityData to 7 Oct 2026

Large Language Models as Zero-Shot Conversational Recommenders

arXiv 2308.10053 ↗First posted 19 Aug 2023cs.IR cs.AIcited by 126

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Abstract (arXiv)

In this paper, we present empirical studies on conversational recommendation tasks using representative large language models in a zero-shot setting with three primary contributions. (1) Data: To gain insights into model behavior in "in-the-wild" conversational recommendation scenarios, we construct a new dataset of recommendation-related conversations by scraping a popular discussion website. This is the largest public real-world conversational recommendation dataset to date. (2) Evaluation: On the new dataset and two existing conversational recommendation datasets, we observe that even without fine-tuning, large language models can outperform existing fine-tuned conversational recommendation models. (3) Analysis: We propose various probing tasks to investigate the mechanisms behind the remarkable performance of large language models in conversational recommendation. We analyze both the large language models' behaviors and the characteristics of the datasets, providing a holistic understanding of the models' effectiveness, limitations and suggesting directions for the design of future conversational recommenders

Key facts. Cited by 126 papers in the map (data through 7 Oct 2026). Builds on 54 of its 75 references that are in the map. First posted on arXiv 19 Aug 2023.

Builds on

54 of 75 references.

References · 54

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  2. 2020Language Models are Few-Shot Learnerscited by 32,199
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38 more
  1. 2018Self-Attentive Sequential Recommendationcited by 1,169
  2. 2017Neural Collaborative Filteringcited by 1,108
  3. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 952
  4. 2019DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generationcited by 842
  5. 2017DailyDialog: A Manually Labelled Multi-turn Dialogue Datasetcited by 649
  6. 2023LaMP: When Large Language Models Meet Personalizationcited by 425
  7. 2022Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)cited by 396
  8. 2023TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendationcited by 336
  9. 2020Autoregressive Entity Retrievalcited by 333
  10. 2020S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximizationcited by 311
  11. 2018Variational Autoencoders for Collaborative Filteringcited by 308
  12. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 307
  13. 2023CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-Xcited by 294
  14. 2023The False Promise of Imitating Proprietary LLMscited by 254
  15. 2022Transformer Memory as a Differentiable Search Indexcited by 242
  16. 2020Bias and Debias in Recommender System: A Survey and Future Directionscited by 239
  17. 2023Is ChatGPT a Good Recommender? A Preliminary Studycited by 192
  18. 2018Towards Deep Conversational Recommendationscited by 174
  19. 2022M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systemscited by 152
  20. 2020Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusioncited by 122
  21. 2023Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Predictioncited by 113
  22. 2019Towards Knowledge-Based Recommender Dialog Systemcited by 112
  23. 2023GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretationcited by 101
  24. 2020Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systemscited by 98
  25. 2020Towards Topic-Guided Conversational Recommender Systemcited by 94
  26. 2020INSPIRED: Toward Sociable Recommendation Dialog Systemscited by 91
  27. 2022Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learningcited by 85
  28. 2020Interactive Path Reasoning on Graph for Conversational Recommendationcited by 72
  29. 2023Generative Recommendation: Towards Next-generation Recommender Paradigmcited by 69
  30. 2023Leveraging Large Language Models in Conversational Recommender Systemscited by 63
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  32. 2023Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Modelscited by 48
  33. 2021RevCore: Review-augmented Conversational Recommendationcited by 47
  34. 2019Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialoguecited by 44
  35. 2022A Survey on Trustworthy Recommender Systemscited by 40
  36. 2020What does BERT know about books, movies and music? Probing BERT for Conversational Recommendationcited by 36
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  38. 2022User-Centric Conversational Recommendation with Multi-Aspect User Modelingcited by 29

50 most cited of 54 All 54 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 9; 2024: 48; 2025: 47; 2026: 22.

126 papers.

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  1. 2025Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactionscited by 236
  2. 2024Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Eracited by 97
  3. 2023Cognitive Overload: Jailbreaking Large Language Models with Overloaded Logical Thinkingcited by 78
  4. 2023Large Language Models for Generative Recommendation: A Survey and Visionary Discussionscited by 72
  5. 2024A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)cited by 58
  6. 2024Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender Systemcited by 50
  7. 2025A Survey on LLM-powered Agents for Recommender Systemscited by 49
  8. 2024ALI-Agent: Assessing LLMs' Alignment with Human Values via Agent-based Evaluationcited by 46
  9. 2024Evaluating Large Language Models as Generative User Simulators for Conversational Recommendationcited by 45
  10. 2023ChatCounselor: A Large Language Models for Mental Health Supportcited by 44
  11. 2023Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestioncited by 43
  12. 2024On the Multi-turn Instruction Following for Conversational Web Agentscited by 42
38 more
  1. 2025Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learningcited by 32
  2. 2025SimUSER: Simulating User Behavior with Large Language Models for Recommender System Evaluationcited by 32
  3. 2025A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrievalcited by 31
  4. 2023Large Language Models for Intent-Driven Session Recommendationscited by 30
  5. 2024Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recencycited by 27
  6. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyondcited by 26
  7. 2024How Reliable is Your Simulator? Analysis on the Limitations of Current LLM-based User Simulators for Conversational Recommendationcited by 25
  8. 2024On Overcoming Miscalibrated Conversational Priors in LLM-based Chatbotscited by 23
  9. 2024A Philosophical Introduction to Language Models -- Part I: Continuity With Classic Debatescited by 22
  10. 2024RDRec: Rationale Distillation for LLM-based Recommendationcited by 21
  11. 2024Self-Updatable Large Language Models by Integrating Context into Model Parameterscited by 21
  12. 2023Evaluating ChatGPT as a Recommender System: A Rigorous Approachcited by 20
  13. 2023User Modeling in the Era of Large Language Models: Current Research and Future Directionscited by 18
  14. 2024Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitationcited by 17
  15. 2025A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigmscited by 17
  16. 2024Federated Recommendation via Hybrid Retrieval Augmented Generationcited by 16
  17. 2024Agentic Feedback Loop Modeling Improves Recommendation and User Simulationcited by 16
  18. 2024Pearl: A Review-driven Persona-Knowledge Grounded Conversational Recommendation Datasetcited by 15
  19. 2024Taxonomy-Guided Zero-Shot Recommendations with LLMscited by 15
  20. 2025Uncertainty Quantification and Decomposition for LLM-based Recommendationcited by 12
  21. 2025Pluralistic Off-policy Evaluation and Alignmentcited by 11
  22. 2024Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendationcited by 10
  23. 2025Graph Retrieval-Augmented LLM for Conversational Recommendation Systemscited by 10
  24. 2026ConvApparel: A Benchmark Dataset and Validation Framework for User Simulators in Conversational Recommenderscited by 10
  25. 2024FAIR Enough: How Can We Develop and Assess a FAIR-Compliant Dataset for Large Language Models' Training?cited by 9
  26. 2024A Fine-tuning Enhanced RAG System with Quantized Influence Measure as AI Judgecited by 9
  27. 2024Towards Empathetic Conversational Recommender Systemscited by 9
  28. 2024Comprehending Knowledge Graphs with Large Language Models for Recommender Systemscited by 9
  29. 2025LLM-based User Profile Management for Recommender Systemcited by 9
  30. 2025In-context Ranking Preference Optimizationcited by 9
  31. 2025From Clicks to Preference: A Multi-stage Alignment Framework for Generative Query Suggestion in Conversational Systemcited by 9
  32. 2023Adaptive In-Context Learning with Large Language Models for Bundle Generationcited by 8
  33. 2024Re2LLM: Reflective Reinforcement Large Language Model for Session-based Recommendationcited by 8
  34. 2024Incorporating External Knowledge and Goal Guidance for LLM-based Conversational Recommender Systemscited by 8
  35. 2024Enhancing High-order Interaction Awareness in LLM-based Recommender Modelcited by 8
  36. 2024LANE: Logic Alignment of Non-tuning Large Language Models and Online Recommendation Systems for Explainable Reason Generationcited by 6
  37. 2024LARR: Large Language Model Aided Real-time Scene Recommendation with Semantic Understandingcited by 6
  38. 2024Who Can Withstand Chat-Audio Attacks? An Evaluation Benchmark for Large Audio-Language Modelscited by 6

50 most cited of 126 All 126 (JSON)