mlsanityData to 7 Oct 2026

LLM-Rec: Personalized Recommendation via Prompting Large Language Models

arXiv 2307.15780 ↗First posted 24 Jul 2023cs.CL cs.AI cs.IRcited by 77

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Abstract (arXiv)

Text-based recommendation holds a wide range of practical applications due to its versatility, as textual descriptions can represent nearly any type of item. However, directly employing the original item descriptions may not yield optimal recommendation performance due to the lack of comprehensive information to align with user preferences. Recent advances in large language models (LLMs) have showcased their remarkable ability to harness commonsense knowledge and reasoning. In this study, we introduce a novel approach, coined LLM-Rec, which incorporates four distinct prompting strategies of text enrichment for improving personalized text-based recommendations. Our empirical experiments reveal that using LLM-augmented text significantly enhances recommendation quality. Even basic MLP (Multi-Layer Perceptron) models achieve comparable or even better results than complex content-based methods. Notably, the success of LLM-Rec lies in its prompting strategies, which effectively tap into the language model's comprehension of both general and specific item characteristics. This highlights the importance of employing diverse prompts and input augmentation techniques to boost the recommendation effectiveness of LLMs.

Key facts. Cited by 77 papers in the map (data through 7 Oct 2026). Builds on 34 of its 66 references that are in the map. First posted on arXiv 24 Jul 2023.

Builds on

34 of 66 references.

References · 34

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  7. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Modelscited by 1,397
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22 more
  1. 2020DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systemscited by 326
  2. 2018AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networkscited by 319
  3. 2023Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender Systemcited by 241
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  5. 2019Assessing BERT's Syntactic Abilitiescited by 191
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  9. 2022ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Modelscited by 141
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 21; 2025: 29; 2026: 23.

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  1. 2023PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPUcited by 210
  2. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)cited by 170
  3. 2024Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8Bcited by 156
  4. 2023Conversational Health Agents: A Personalized LLM-Powered Agent Frameworkcited by 91
  5. 2024Fiddler: CPU-GPU Orchestration for Fast Inference of Mixture-of-Experts Modelscited by 69
  6. 2024HYDRA: Model Factorization Framework for Black-Box LLM Personalizationcited by 59
  7. 2024MC-LLaVA: Multi-Concept Personalized Vision-Language Modelcited by 53
  8. 2025SocioVerse: A World Model for Social Simulation Powered by LLM Agents and A Pool of 10 Million Real-World Userscited by 50
  9. 2024Personalized Large Language Modelscited by 44
  10. 2024Graph Machine Learning in the Era of Large Language Models (LLMs)cited by 33
  11. 2025Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learningcited by 32
  12. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyondcited by 26
38 more
  1. 2024Unified Parameter-Efficient Unlearning for LLMscited by 24
  2. 2025Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directionscited by 23
  3. 2025AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corporacited by 23
  4. 2024ChatDiet: Empowering Personalized Nutrition-Oriented Food Recommender Chatbots through an LLM-Augmented Frameworkcited by 20
  5. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Surveycited by 20
  6. 2023User Modeling in the Era of Large Language Models: Current Research and Future Directionscited by 18
  7. 2024Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyondcited by 18
  8. 2025Personalize Your LLM: Fake it then Align itcited by 15
  9. 2025Evaluating Long-Context Reasoning in LLM-Based WebAgentscited by 15
  10. 2024Towards Graph Foundation Models for Personalizationcited by 12
  11. 2024Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Reviewcited by 11
  12. 2024Representation Bias in Political Sample Simulations with Large Language Modelscited by 10
  13. 2025ProPerSim: Developing Proactive and Personalized AI Assistants through User-Assistant Simulationcited by 10
  14. 2025MR.Rec: Synergizing Memory and Reasoning for Personalized Recommendation Assistant with LLMscited by 9
  15. 2024Unveiling Bias in Fairness Evaluations of Large Language Models: A Critical Literature Review of Music and Movie Recommendation Systemscited by 7
  16. 2025Collab-REC: An LLM-based Agentic Framework for Balancing Recommendations in Tourismcited by 7
  17. 2025TrackRec: Iterative Alternating Feedback with Chain-of-Thought via Preference Alignment for Recommendationcited by 7
  18. 2023GPT-4V(ision) as A Social Media Analysis Enginecited by 6
  19. 2024Harnessing GPT-4V(ision) for Insurance: A Preliminary Explorationcited by 5
  20. 2025STMA: A Spatio-Temporal Memory Agent for Long-Horizon Embodied Task Planningcited by 5
  21. 2025Learning from Natural Language Feedback for Personalized Question Answeringcited by 5
  22. 2024Large Language Model-based Role-Playing for Personalized Medical Jargon Extractioncited by 4
  23. 2024Collaborative Cross-modal Fusion with Large Language Model for Recommendationcited by 4
  24. 2024LSAQ: Layer-Specific Adaptive Quantization for Large Language Model Deploymentcited by 4
  25. 2025FlexInfer: Breaking Memory Constraint via Flexible and Efficient Offloading for On-Device LLM Inferencecited by 4
  26. 2025Exploring Backdoor Attack and Defense for LLM-empowered Recommendationscited by 4
  27. 2026Agentic Recommender System with Hierarchical Belief-State Memorycited by 4
  28. 2024ABC Align: Large Language Model Alignment for Safety & Accuracycited by 3
  29. 2025Through the Prism of Culture: Evaluating LLMs' Understanding of Indian Subcultures and Traditionscited by 3
  30. 2025PerFairX: Is There a Balance Between Fairness and Personality in Large Language Model Recommendations?cited by 3
  31. 2026Conv-FinRe: A Conversational and Longitudinal Benchmark for Utility-Grounded Financial Recommendationcited by 3
  32. 2026EXACT: Explicit Attribute-Guided Decoding-Time Personalizationcited by 3
  33. 2026ReRec: Reasoning-Augmented LLM-based Recommendation Assistant via Reinforcement Fine-tuningcited by 3
  34. 2026Personalize Your Large Vision-language Models With In-context Prompt Tuningcited by 3
  35. 2024EACO-RAG: Towards Distributed Tiered LLM Deployment using Edge-Assisted and Collaborative RAG with Adaptive Knowledge Updatecited by 2
  36. 2025UC-MOA: Utility-Conditioned Multi-Objective Alignment for Distributional Pareto-Optimalitycited by 2
  37. 2025Continual Low-Rank Adapters for LLM-based Generative Recommender Systemscited by 2
  38. 2026Do LLMs Benefit from User and Item Embeddings in Recommendation Tasks?cited by 2

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