mlsanityData to 7 Oct 2026

Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System

arXiv 2303.14524 ↗First posted 25 Mar 2023cs.IR cs.CL cs.LGcited by 241

Not on the map. Why?

Cited by 32 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Large language models (LLMs) have demonstrated their significant potential to be applied for addressing various application tasks. However, traditional recommender systems continue to face great challenges such as poor interactivity and explainability, which actually also hinder their broad deployment in real-world systems. To address these limitations, this paper proposes a novel paradigm called Chat-Rec (ChatGPT Augmented Recommender System) that innovatively augments LLMs for building conversational recommender systems by converting user profiles and historical interactions into prompts. Chat-Rec is demonstrated to be effective in learning user preferences and establishing connections between users and products through in-context learning, which also makes the recommendation process more interactive and explainable. What's more, within the Chat-Rec framework, user's preferences can transfer to different products for cross-domain recommendations, and prompt-based injection of information into LLMs can also handle the cold-start scenarios with new items. In our experiments, Chat-Rec effectively improve the results of top-k recommendations and performs better in zero-shot rating prediction task. Chat-Rec offers a novel approach to improving recommender systems and presents new practical scenarios for the implementation of AIGC (AI generated content) in recommender system studies.

Key facts. Cited by 241 papers in the map (data through 7 Oct 2026). Builds on 19 of its 28 references that are in the map. First posted on arXiv 25 Mar 2023.

Builds on

19 of 28 references.

References · 19

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  4. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  5. 2021Evaluating Large Language Models Trained on Codecited by 8,053
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  7. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
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  11. 2020UnifiedQA: Crossing Format Boundaries With a Single QA Systemcited by 475
  12. 2022Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)cited by 396
7 more
  1. 2022Generate rather than Retrieve: Large Language Models are Strong Context Generatorscited by 296
  2. 2022TALM: Tool Augmented Language Modelscited by 197
  3. 2021Cross-Domain Recommendation: Challenges, Progress, and Prospectscited by 74
  4. 2021Personalized Transformer for Explainable Recommendationcited by 58
  5. 2023Pre-train, Prompt and Recommendation: A Comprehensive Survey of Language Modelling Paradigm Adaptations in Recommender Systemscited by 45
  6. 2020Fusing Context Into Knowledge Graph for Commonsense Question Answeringcited by 33
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 52; 2024: 73; 2025: 65; 2026: 51.

241 papers.

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  1. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  2. 2023A Survey on Large Language Models for Recommendationcited by 378
  3. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 307
  4. 2023Exploring the Potential of Large Language Models (LLMs) in Learning on Graphscited by 271
  5. 2023MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Usecited by 209
  6. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 188
  7. 2024A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systemscited by 170
  8. 2023Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approachcited by 168
  9. 2023Representation Learning with Large Language Models for Recommendationcited by 155
  10. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 141
  11. 2023Recommender AI Agent: Integrating Large Language Models for Interactive Recommendationscited by 130
  12. 2024Language agents achieve superhuman synthesis of scientific knowledgecited by 121
38 more
  1. 2023AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systemscited by 116
  2. 2023Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Predictioncited by 113
  3. 2023Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendationcited by 113
  4. 2023Aligning Language Models to User Opinionscited by 98
  5. 2023When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunitiescited by 89
  6. 2023LLM-Rec: Personalized Recommendation via Prompting Large Language Modelscited by 77
  7. 2023ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendationcited by 76
  8. 2023Large Language Models for Generative Recommendation: A Survey and Visionary Discussionscited by 72
  9. 2024Personalized Pieces: Efficient Personalized Large Language Models through Collaborative Effortscited by 71
  10. 2023Penetrative AI: Making LLMs Comprehend the Physical Worldcited by 64
  11. 2024On Softmax Direct Preference Optimization for Recommendationcited by 64
  12. 2024A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)cited by 58
  13. 2024Determinants of LLM-assisted Decision-Makingcited by 55
  14. 2023LlamaRec: Two-Stage Recommendation using Large Language Models for Rankingcited by 52
  15. 2024IDGenRec: LLM-RecSys Alignment with Textual ID Learningcited by 50
  16. 2024Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender Systemcited by 50
  17. 2024LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendationcited by 47
  18. 2025Reinforced Latent Reasoning for LLM-based Recommendationcited by 46
  19. 2024A Multi-Agent Conversational Recommender Systemcited by 42
  20. 2024Modular RAG: Transforming RAG Systems into LEGO-like Reconfigurable Frameworkscited by 41
  21. 2023GeoGPT: Understanding and Processing Geospatial Tasks through An Autonomous GPTcited by 40
  22. 2023E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendationcited by 39
  23. 2023Augmented Large Language Models with Parametric Knowledge Guidingcited by 37
  24. 2024TokenRec: Learning to Tokenize ID for LLM-based Generative Recommendationcited by 35
  25. 2025Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learningcited by 32
  26. 2025Generative to Agentic AI: Survey, Conceptualization, and Challengescited by 32
  27. 2023A Large Language Model Enhanced Conversational Recommender Systemcited by 31
  28. 2023RAH! RecSys-Assistant-Human: A Human-Centered Recommendation Framework with LLM Agentscited by 30
  29. 2023Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Communitycited by 29
  30. 2023CodeCoT: Tackling Code Syntax Errors in CoT Reasoning for Code Generationcited by 29
  31. 2023LLMRec: Benchmarking Large Language Models on Recommendation Taskcited by 29
  32. 2023DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendationcited by 27
  33. 2024Leveraging LLM Reasoning Enhances Personalized Recommender Systemscited by 26
  34. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyondcited by 26
  35. 2024Large Language Model Distilling Medication Recommendation Modelcited by 25
  36. 2024How Reliable is Your Simulator? Analysis on the Limitations of Current LLM-based User Simulators for Conversational Recommendationcited by 25
  37. 2024LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendationscited by 24
  38. 2023CRUSH4SQL: Collective Retrieval Using Schema Hallucination For Text2SQLcited by 23

50 most cited of 241 All 241 (JSON)