智能系统学报2026，Vol.21Issue(1)：19-40,22.DOI:10.11992/tis.202505006
生成式推荐系统综述
A survey of generative recommender systems
摘要
Abstract
With the rapid growth of social media content scale,traditional collaborative filtering recommender systems increasingly exhibit limitations in data sparsity and cold start problems.In recent years,the powerful data feature analys-is and content generation capabilities of generative models have brought new development opportunities for recom-mender systems.This paper systematically reviews the technical frameworks and research progress in generative recom-mender systems,focusing on five key aspects:feature tokenization methods,core model architectural designs,main-stream evaluation protocols and typical application scenarios.Through comparative analysis and literature review,we demonstrate that generative recommender systems significantly outperform conventional approaches in recommenda-tion accuracy,personality,and scenario adaptability.The study further identifies critical challenges including computa-tional overhead,privacy risks,and standardization of evaluation metrics.Practical solutions and future research direc-tions are proposed to address these challenges,breaking the cognitive bottleneck of generative recommender systems.关键词
推荐系统/生成式模型/大语言模型/特征标记/表示学习/模型架构/协同信息/评估方法Key words
recommender system/generative model/large language model/feature tokenization/representation learning/model architecture/collaborative information/evaluation method分类
信息技术与安全科学引用本文复制引用
石磊,赵雨秋,袁瑞萍,钟岩,刘艳超..生成式推荐系统综述[J].智能系统学报,2026,21(1):19-40,22.基金项目
北京物资学院系统科学研究院开放课题(BWUISS35) （BWUISS35）
国家重点研发计划项目(2022YFC3302103). （2022YFC3302103）
