mlsanityData to 14 Oct 2026

A Survey on Large Language Models for Personalized and Explainable Recommendations

arXiv 2311.12338 ↗First posted 21 Nov 2023cs.IR cs.AIcited by 11

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Abstract (arXiv)

In recent years, Recommender Systems(RS) have witnessed a transformative shift with the advent of Large Language Models(LLMs) in the field of Natural Language Processing(NLP). These models such as OpenAI's GPT-3.5/4, Llama from Meta, have demonstrated unprecedented capabilities in understanding and generating human-like text. This has led to a paradigm shift in the realm of personalized and explainable recommendations, as LLMs offer a versatile toolset for processing vast amounts of textual data to enhance user experiences. To provide a comprehensive understanding of the existing LLM-based recommendation systems, this survey aims to analyze how RS can benefit from LLM-based methodologies. Furthermore, we describe major challenges in Personalized Explanation Generating(PEG) tasks, which are cold-start problems, unfairness and bias problems in RS.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 15 of its 22 references that are in the map. First posted on arXiv 21 Nov 2023.

Builds on

15 of 22 references.

References · 15

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  4. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
  5. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 964
  6. 2022STaR: Bootstrapping Reasoning With Reasoningcited by 940
  7. 2022Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)cited by 402
  8. 2023A Survey on Large Language Models for Recommendationcited by 379
  9. 2020Bias and Debias in Recommender System: A Survey and Future Directionscited by 240
  10. 2022Towards Universal Sequence Representation Learning for Recommender Systemscited by 121
  11. 2022Personalized Prompt Learning for Explainable Recommendationcited by 79
  12. 2023Pre-train, Prompt and Recommendation: A Comprehensive Survey of Language Modelling Paradigm Adaptations in Recommender Systemscited by 45
3 more
  1. 2023UP5: Unbiased Foundation Model for Fairness-aware Recommendationcited by 38
  2. 2023Generative Sequential Recommendation with GPTReccited by 21
  3. 2023Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start Recommendationscited by 15

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Cited by year

Citing papers per year: 2024: 8; 2025: 3.

11 papers.

  1. 2024Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalizationcited by 208
  2. 2024Personalized Language Modeling from Personalized Human Feedbackcited by 132
  3. 2024A Comprehensive Survey of Foundation Models in Medicinecited by 56
  4. 2025Reasoning-Enhanced Self-Training for Long-Form Personalized Text Generationcited by 27
  5. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyondcited by 26
  6. 2024Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Modelscited by 21
  7. 2024A Survey on Diffusion Models for Recommender Systemscited by 20
  8. 2024Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential Recommendationcited by 19
  9. 2025Iterative Critique-Refine Framework for Enhancing LLM Personalizationcited by 1
  10. 2024GCOF: Self-iterative Text Generation for Copywriting Using Large Language Modelcited by 0
  11. 2025From latent factors to language: a user study on LLM-generated explanations for an inherently interpretable matrix-based recommender systemcited by 0