mlsanityData to 14 Oct 2026

PALR: Personalization Aware LLMs for Recommendation

arXiv 2305.07622 ↗First posted 12 May 2023cs.IR cs.AI cs.CLcited by 96

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Abstract (arXiv)

Large language models (LLMs) have recently received significant attention for their exceptional capabilities. Despite extensive efforts in developing general-purpose LLMs that can be utilized in various natural language processing (NLP) tasks, there has been less research exploring their potential in recommender systems. In this paper, we propose a novel framework, named PALR, which aiming to combine user history behaviors (such as clicks, purchases, ratings, etc.) with LLMs to generate user preferred items. Specifically, we first use user/item interactions as guidance for candidate retrieval. Then we adopt a LLM-based ranking model to generate recommended items. Unlike existing approaches that typically adopt general-purpose LLMs for zero/few-shot recommendation testing or training on small-sized language models (with less than 1 billion parameters), which cannot fully elicit LLMs' reasoning abilities and leverage rich item side parametric knowledge, we fine-tune a 7 billion parameters LLM for the ranking purpose. This model takes retrieval candidates in natural language format as input, with instruction which explicitly asking to select results from input candidates during inference. Our experimental results demonstrate that our solution outperforms state-of-the-art models on various sequential recommendation tasks.

Key facts. Cited by 96 papers in the map (data through 14 Oct 2026). Builds on 15 of its 30 references that are in the map. First posted on arXiv 12 May 2023.

Builds on

15 of 30 references.

References · 15

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  4. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,780
  5. 2022Scaling Instruction-Finetuned Language Modelscited by 3,024
  6. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,724
  7. 2018Self-Attentive Sequential Recommendationcited by 1,184
  8. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
  9. 2017Neural Collaborative Filteringcited by 1,111
  10. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 964
  11. 2018Personalized Top-N Sequential Recommendation via Convolutional Sequence Embeddingcited by 476
  12. 2023Is ChatGPT a Good Recommender? A Preliminary Studycited by 193
3 more
  1. 2022M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systemscited by 152
  2. 2023GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretationcited by 103
  3. 2021Dynamic Memory based Attention Network for Sequential Recommendationcited by 16

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 21; 2024: 35; 2025: 30; 2026: 10.

96 papers.

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  1. 2023A Survey on Large Language Models for Recommendationcited by 379
  2. 2024Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Securitycited by 260
  3. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 189
  4. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 142
  5. 2023RecMind: Large Language Model Powered Agent For Recommendationcited by 129
  6. 2023AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systemscited by 116
  7. 2023When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunitiescited by 90
  8. 2023LLM-Rec: Personalized Recommendation via Prompting Large Language Modelscited by 77
  9. 2023ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendationcited by 77
  10. 2023Large Language Models for Generative Recommendation: A Survey and Visionary Discussionscited by 73
  11. 2024On Softmax Direct Preference Optimization for Recommendationcited by 64
  12. 2024HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modelingcited by 63
38 more
  1. 2024A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)cited by 58
  2. 2023LlamaRec: Two-Stage Recommendation using Large Language Models for Rankingcited by 53
  3. 2024Evaluating Large Language Models as Generative User Simulators for Conversational Recommendationcited by 48
  4. 2023Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Modelscited by 45
  5. 2024Personalized Large Language Modelscited by 44
  6. 2023Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestioncited by 43
  7. 2024Stereotype or Personalization? User Identity Biases Chatbot Recommendationscited by 34
  8. 2025Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learningcited by 33
  9. 2024AI PERSONA: Towards Life-long Personalization of LLMscited by 32
  10. 2023Learning Personalized Alignment for Evaluating Open-ended Text Generationcited by 29
  11. 2023DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendationcited by 27
  12. 2024Exploring Safety-Utility Trade-Offs in Personalized Language Modelscited by 27
  13. 2024Large Language Model Distilling Medication Recommendation Modelcited by 26
  14. 2025CUPID: Evaluating Personalized and Contextualized Alignment of LLMs from Interactionscited by 22
  15. 2024On the Essence and Prospect: An Investigation of Alignment Approaches for Big Modelscited by 21
  16. 2024Aligning Large Language Models for Controllable Recommendationscited by 19
  17. 2024Guided Profile Generation Improves Personalization with LLMscited by 19
  18. 2023User Modeling in the Era of Large Language Models: Current Research and Future Directionscited by 18
  19. 2024Language-Based User Profiles for Recommendationcited by 18
  20. 2024Language Representations Can be What Recommenders Need: Findings and Potentialscited by 18
  21. 2024Exploring the Frontiers of LLMs in Psychological Applications: A Comprehensive Reviewcited by 17
  22. 2024Lifelong Personalized Low-Rank Adaptation of Large Language Models for Recommendationcited by 17
  23. 2024Federated Recommendation via Hybrid Retrieval Augmented Generationcited by 16
  24. 2024A Survey of Generative Search and Recommendation in the Era of Large Language Modelscited by 15
  25. 2024RAG based Question-Answering for Contextual Response Prediction Systemcited by 14
  26. 2024ReasoningRec: Bridging Personalized Recommendations and Human-Interpretable Explanations through LLM Reasoningcited by 14
  27. 2024Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Reviewcited by 12
  28. 2025Uncertainty Quantification and Decomposition for LLM-based Recommendationcited by 12
  29. 2024Make Large Language Model a Better Rankercited by 11
  30. 2025LettinGo: Explore User Profile Generation for Recommendation Systemcited by 11
  31. 2025ProPerSim: Developing Proactive and Personalized AI Assistants through User-Assistant Simulationcited by 11
  32. 2024SINKT: A Structure-Aware Inductive Knowledge Tracing Model with Large Language Modelcited by 10
  33. 2024End-to-end Training for Recommendation with Language-based User Profilescited by 10
  34. 2023RoleCraft-GLM: Advancing Personalized Role-Playing in Large Language Modelscited by 9
  35. 2025Exploring Persona-dependent LLM Alignment for the Moral Machine Experimentcited by 9
  36. 2023Graph Meets LLM: A Novel Approach to Collaborative Filtering for Robust Conversational Understandingcited by 8
  37. 2023Adaptive In-Context Learning with Large Language Models for Bundle Generationcited by 8
  38. 2024Token-level Proximal Policy Optimization for Query Generationcited by 7

50 most cited of 96 All 96 (JSON)

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  1. 2025A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challenges
  2. 2024Aligning Large Language Models with Recommendation Knowledge
  3. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond
  4. 2025Generative Large Recommendation Models: Emerging Trends in LLMs for Recommendation
  5. 2024All Roads Lead to Rome: Unveiling the Trajectory of Recommender Systems Across the LLM Era
  6. 2023Empowering Few-Shot Recommender Systems with Large Language Models -- Enhanced Representations
  7. 2023ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models
  8. 2025MR.Rec: Synergizing Memory and Reasoning for Personalized Recommendation Assistant with LLMs