mlsanityData to 7 Oct 2026

RecMind: Large Language Model Powered Agent For Recommendation

arXiv 2308.14296 ↗First posted 28 Aug 2023cs.IR cs.AIcited by 129

On the map: LLM agents and tool use → Agentic recommender systems

Abstract (arXiv)

While the recommendation system (RS) has advanced significantly through deep learning, current RS approaches usually train and fine-tune models on task-specific datasets, limiting their generalizability to new recommendation tasks and their ability to leverage external knowledge due to model scale and data size constraints. Thus, we designed an LLM-powered autonomous recommender agent, RecMind, which is capable of leveraging external knowledge, utilizing tools with careful planning to provide zero-shot personalized recommendations. We propose a Self-Inspiring algorithm to improve the planning ability. At each intermediate step, the LLM self-inspires to consider all previously explored states to plan for the next step. This mechanism greatly improves the model's ability to comprehend and utilize historical information in planning for recommendation. We evaluate RecMind's performance in various recommendation scenarios. Our experiment shows that RecMind outperforms existing zero/few-shot LLM-based recommendation baseline methods in various tasks and achieves comparable performance to a fully trained recommendation model P5.

Key facts. Cited by 129 papers in the map (data through 7 Oct 2026). Placed in Agentic recommender systems, one of 16 directions in LLM agents and tool use. Builds on 30 of its 44 references that are in the map. First posted on arXiv 28 Aug 2023.

Builds on

30 of 44 references.

References · 30

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  4. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  5. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  6. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  7. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  8. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  9. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  10. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
  11. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,722
  12. 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,302
18 more
  1. 2023HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Facecited by 1,173
  2. 2018Self-Attentive Sequential Recommendationcited by 1,169
  3. 2023PaLM 2 Technical Reportcited by 1,154
  4. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,134
  5. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 952
  6. 2016Wide & Deep Learning for Recommender Systemscited by 745
  7. 2015Session-based Recommendations with Recurrent Neural Networkscited by 685
  8. 2022Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)cited by 396
  9. 2023TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendationcited by 336
  10. 2020S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximizationcited by 311
  11. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 307
  12. 2023Is ChatGPT a Good Recommender? A Preliminary Studycited by 192
  13. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 188
  14. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 141
  15. 2017Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networkscited by 126
  16. 2023Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Predictioncited by 113
  17. 2023PALR: Personalization Aware LLMs for Recommendationcited by 96
  18. 2023Zero-Shot Next-Item Recommendation using Large Pretrained Language Modelscited by 53

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 9; 2024: 33; 2025: 43; 2026: 44.

129 papers.

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  1. 2024A Survey on the Memory Mechanism of Large Language Model based Agentscited by 622
  2. 2024Understanding the planning of LLM agents: A surveycited by 414
  3. 2025Large Language Model Agent: A Survey on Methodology, Applications and Challengescited by 194
  4. 2025A Survey of Context Engineering for Large Language Modelscited by 120
  5. 2024Empowering Biomedical Discovery with AI Agentscited by 117
  6. 2023AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systemscited by 116
  7. 2025G-Memory: Tracing Hierarchical Memory for Multi-Agent Systemscited by 112
  8. 2023On Generative Agents in Recommendationcited by 88
  9. 2023NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity Classescited by 78
  10. 2024From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agentscited by 76
  11. 2023Large Language Models for Generative Recommendation: A Survey and Visionary Discussionscited by 72
  12. 2024Large Language Models Empowered Personalized Web Agentscited by 64
38 more
  1. 2024A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)cited by 58
  2. 2025Evaluating LLM-based Agents for Multi-Turn Conversations: A Surveycited by 56
  3. 2025A Survey on LLM-powered Agents for Recommender Systemscited by 49
  4. 2024ALI-Agent: Assessing LLMs' Alignment with Human Values via Agent-based Evaluationcited by 46
  5. 2025Reinforced Latent Reasoning for LLM-based Recommendationcited by 46
  6. 2024eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Datacited by 44
  7. 2023Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestioncited by 43
  8. 2024KARMA: Augmenting Embodied AI Agents with Long-and-short Term Memory Systemscited by 38
  9. 2023LLM as OS, Agents as Apps: Envisioning AIOS, Agents and the AIOS-Agent Ecosystemcited by 35
  10. 2024Do LLM Agents Have Regret? A Case Study in Online Learning and Gamescited by 33
  11. 2025A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrievalcited by 31
  12. 2023DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendationcited by 27
  13. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyondcited by 26
  14. 2025Towards Agentic Recommender Systems in the Era of Multimodal Large Language Modelscited by 25
  15. 2024LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendationscited by 24
  16. 2025AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systemscited by 23
  17. 2026Graph-based Agent Memory: Taxonomy, Techniques, and Applicationscited by 22
  18. 2023Evaluating ChatGPT as a Recommender System: A Rigorous Approachcited by 20
  19. 2026RealMem: Benchmarking LLMs in Real-World Memory-Driven Interactioncited by 20
  20. 2024Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential Recommendationcited by 19
  21. 2025Shop-R1: Rewarding LLMs to Simulate Human Behavior in Online Shopping via Reinforcement Learningcited by 19
  22. 2023User Modeling in the Era of Large Language Models: Current Research and Future Directionscited by 18
  23. 2025A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigmscited by 17
  24. 2024Agentic Feedback Loop Modeling Improves Recommendation and User Simulationcited by 16
  25. 2025PAARS: Persona Aligned Agentic Retail Shopperscited by 14
  26. 2025The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systemscited by 14
  27. 2025AgentSociety Challenge: Designing LLM Agents for User Modeling and Recommendation on Web Platformscited by 12
  28. 2024Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Reviewcited by 11
  29. 2024SUBER: An RL Environment with Simulated Human Behavior for Recommender Systemscited by 11
  30. 2024Knowledge Graph Enhanced Language Agents for Recommendationcited by 11
  31. 2025LLM-Enhanced Multimodal Fusion for Cross-Domain Sequential Recommendationcited by 11
  32. 2026Memex(RL): Scaling Long-Horizon LLM Agents via Indexed Experience Memorycited by 11
  33. 2025Multi-Agent Collaborative Filtering: Orchestrating Users and Items for Agentic Recommendationscited by 10
  34. 2024Step-Back Profiling: Distilling User History for Personalized Scientific Writingcited by 9
  35. 2025Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMscited by 9
  36. 2024STAR: A Simple Training-free Approach for Recommendations using Large Language Modelscited by 8
  37. 2024Causality-Enhanced Behavior Sequence Modeling in LLMs for Personalized Recommendationcited by 8
  38. 2024Exploring the Potential of Large Language Models for Improving Digital Forensic Investigation Efficiencycited by 7

50 most cited of 129 All 129 (JSON)

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  1. 2026Self-Evolving Recommendation System: End-To-End Autonomous Model Optimization With LLM Agents
  2. 2025AgentRec: Next-Generation LLM-Powered Multi-Agent Collaborative Recommendation with Adaptive Intelligence
  3. 2026RecThinker: An Agentic Framework for Tool-Augmented Reasoning in Recommendation
  4. 2025MR.Rec: Synergizing Memory and Reasoning for Personalized Recommendation Assistant with LLMs
  5. 2023Leveraging Large Language Models for Pre-trained Recommender Systems
  6. 2026Auto-RecSys: Harnessing Autonomous Research Agents for Industry-Scale Recommender System
  7. 2023Empowering Few-Shot Recommender Systems with Large Language Models -- Enhanced Representations
  8. 2025A Survey on Generative Recommendation: Data, Model, and Tasks