Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (6): 281-303.doi: 10.11896/jsjkx.250900077

• Database & Big Data & Data Science • Previous Articles     Next Articles

Survey of Recommendation Systems Based on Large Language Models

SHI Hongxu, LIU Yi, LIU Kun   

  1. Academy of Military Sciences,Beijing 100091,China
  • Received:2025-09-11 Revised:2026-02-24 Online:2026-06-15 Published:2026-06-09
  • About author:SHI Hongxu,born in 2002,postgraduate.His main research interests include large language models and recommendation systems.
    LIU Kun,born in 1982,Ph.D,associate professor.His main research interests include big data and artificial intelligence.
  • Supported by:
    Young Scientists Fund of the National Natural Science Foundation of China(72201275) and 8th Young Elite Scientists Sponsorship Program by CAST(2022QNRC001).

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Abstract: With the proliferation of Internet applications and the exacerbation of information overload,recommendation systems have been playing an increasingly important role in providing personalized services and enhancing user experience.Although traditional recommendation methods and deep learning techniques have made remarkable progress in modeling user-item interactions,they still suffer from limitations such as data sparsity,cold start problems,and insufficient understanding of users' deep intents.In recent years,large language models,armed with exceptional capabilities in semantic understanding,knowledge transfer,generation,and logical reasoning,have opened up new directions for recommendation system research.This paper presents a comprehensive survey of the research progress on recommendation systems based on large language models,focusing on analyzing their integration paths and technological evolution in the recommendation process.It covers key stages including feature engineering and data augmentation,feature encoding and semantic representation,recommendation result generation,system-user interaction,and coordinated control of the recommendation process,revealing the unique advantages of large language models in improving recommendation accuracy,interpretability,and dynamic adaptability.Furthermore,this paper discusses practical challenges such as model computational efficiency,knowledge credibility,system fairness,and data security,and prospects the future development directions.Through systematic organization and in-depth analysis,this paper aims to provide references and insights for theoretical innovation and practical applications in the field of recommendation systems.

Key words: Large language models, Recommendation systems, Sequential recommendation, Explainable recommendation, Conversational recommendation, Multimodal recommendation

CLC Number: 

  • TP391
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