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[1]石磊,赵雨秋,袁瑞萍,等.生成式推荐系统综述[J].智能系统学报,2026,21(1):19-40.[doi:10.11992/tis.202505006]
　SHI Lei,ZHAO Yuqiu,YUAN Ruiping,et al.A survey of generative recommender systems[J].CAAI Transactions on Intelligent Systems,2026,21(1):19-40.[doi:10.11992/tis.202505006]

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生成式推荐系统综述

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《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 21 期数: 2026年第1期页码: 19-40 栏目: 综述出版日期: 2026-03-05

Title:: A survey of generative recommender systems

作者:: 石磊¹, 赵雨秋¹, 袁瑞萍², 钟岩³, 刘艳超¹; 1. 中国传媒大学媒体融合与传播国家重点实验室, 北京 100024;
2. 北京物资学院计算机与人工智能学院, 北京 101149;
3. 北京大学数学科学学院, 北京 100871

Author(s):: SHI Lei¹, ZHAO Yuqiu¹, YUAN Ruiping², ZHONG Yan³, LIU Yanchao¹; 1. State Key Laboratory of Media Integration and Communication, Communication University of China, Beijing 100024, China;
2. School of Computer and Artificial Intelligence, Beijing Wuzi University, Beijing 101149, China;
3. School of Mathematical Sciences, Peking University, Beijing 100871, China

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

Keywords:: recommender system; generative model; large language model; feature tokenization; representation learning; model architecture; collaborative information; evaluation method

分类号:: TP301

DOI:: 10.11992/tis.202505006

摘要:: 随着社交媒体内容规模的急剧增长，传统协同过滤推荐系统在数据稀疏性和冷启动等方面的局限性日益凸显。近年来，生成式模型强大的数据特征分析与内容生成能力，为推荐系统带来新的发展机遇。本文系统性地综述了生成式推荐系统的技术框架与研究进展，重点阐述了生成式推荐系统的特征标记方法、核心模型架构、主流评估方案以及典型的应用场景。通过对比分析与文献研究，论证了生成式推荐系统在推荐准确性、个性化和场景适应性等方面的显著优势。最后，本文深入探讨了当前研究面临的关键挑战，包括计算资源消耗、隐私安全风险以及评估标准统一性等问题，并对未来研究方向提出建设性展望，为突破生成式推荐系统的认知瓶颈提出了创新性视角。

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 analysis and content generation capabilities of generative models have brought new development opportunities for recommender systems. This paper systematically reviews the technical frameworks and research progress in generative recommender systems, focusing on five key aspects: feature tokenization methods, core model architectural designs, mainstream evaluation protocols and typical application scenarios. Through comparative analysis and literature review, we demonstrate that generative recommender systems significantly outperform conventional approaches in recommendation accuracy, personality, and scenario adaptability. The study further identifies critical challenges including computational overhead, privacy risks, and standardization of evaluation metrics. Practical solutions and future research directions are proposed to address these challenges, breaking the cognitive bottleneck of generative recommender systems.

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备注/Memo

收稿日期:2025-5-14。
基金项目:北京物资学院系统科学研究院开放课题(BWUISS35)；国家重点研发计划项目(2022YFC3302103).
作者简介:石磊，副研究员，中国人工智能学会智能服务专委会委员，主要研究方向为智能信息处理、大数据分析与挖掘、社交网络搜索及人工智能。发表学术论文40余篇。E-mail：leiky_shi@cuc.edu.cn。;赵雨秋，硕士研究生，主要研究方向为推荐系统与信息检索。E-mail：yuqiuzhao@mails.cuc.edu.cn。;袁瑞萍，教授，主要研究方向为复杂物流系统、数据分析与智能决策。主持国家自然科学基金项目1项，发表学术论文40余篇。E-mail：yuanruiping@bwu.edu.cn。
通讯作者:袁瑞萍. E-mail：yuanruiping@bwu.edu.cn

更新日期/Last Update: 2026-01-05

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