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[1]吴国栋,谢东辰,黄雯婧,等.检索增强生成推荐及其研究进展[J].智能系统学报,2026,21(3):577-597.[doi:10.11992/tis.202508007]
　WU Guodong,XIE Dongchen,HUANG Wenjing,et al.Retrieval-augmented generation for recommendation and research progress[J].CAAI Transactions on Intelligent Systems,2026,21(3):577-597.[doi:10.11992/tis.202508007]

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检索增强生成推荐及其研究进展

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《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 21 期数: 2026年第3期页码: 577-597 栏目: 综述出版日期: 2026-05-05

Title:: Retrieval-augmented generation for recommendation and research progress

作者:: 吴国栋, 谢东辰, 黄雯婧, 郑阳, 涂立静; 安徽农业大学人工智能学院, 安徽合肥 230036

Author(s):: WU Guodong, XIE Dongchen, HUANG Wenjing, ZHENG Yang, TU Lijing; School of Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China

关键词:: 检索增强生成; 推荐; 大语言模型; 检索; 外部知识; 深度学习; 知识库; 表征学习

Keywords:: RAG; recommendation; LLM; retrieval; external knowledge; deep learning; knowledge base; representation learning

分类号:: TP301

DOI:: 10.11992/tis.202508007

文献标志码:: 2026-3-12

摘要:: 检索增强生成（retrieval-augmented generation，RAG）推荐，作为一种新兴推荐范式，已引起学术界广泛关注。本文在分析RAG推荐及流程基础上，从基于内容的RAG推荐、协同过滤RAG推荐、行为序列RAG推荐、智能体RAG推荐4个维度，深入探讨了现有RAG推荐研究的主要进展、技术特点与适用场景。同时，指出了当前RAG推荐研究在检索效率与生成质量的平衡性、多源上下文信息的高效整合、知识库的实时自动更新机制以及用户隐私保护等方面存在的问题。基于上述分析，从多源多模态信息融合、检索-生成协同优化、动态自适应机制构建以及隐私保护增强等视角，提出了RAG推荐未来的主要研究方向。

Abstract:: Retrieval-augmented generation (RAG) recommendation has emerged as a new recommendation paradigm and has attracted extensive academic attention. Based on an analysis of RAG recommendation and its process, this paper examines the main progress, technical features, and applicable scenarios of existing RAG recommendation research across four dimensions: content-based RAG recommendation, collaborative filtering RAG recommendation, behavioral sequence RAG recommendation, and agent-based RAG recommendation. It also identifies key open problems in current RAG recommendation research, including the trade-off between retrieval efficiency and generation quality, the efficient integration of multi-source contextual information, real-time automatic updating of the knowledge base, and user privacy protection. Based on the above analysis, this paper proposes the main future research directions for RAG recommendation from the perspectives of multi-source and multi-modal information fusion, collaborative optimization of generation and retrieval, construction of dynamic adaptive mechanisms, and enhancement of privacy protection.

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备注/Memo

收稿日期:2025-8-6。
基金项目:国家自然科学基金项目(32371993)；安徽省高校自然科学研究重点项目(2024AH050443)；安徽省自然科学基金项目(2108085MF209)；安徽省科技重大专项项目(202103b06020013).
作者简介:吴国栋，副教授，博士，主要研究方向为人工智能、推荐系统。主持安徽省科技重大专项项目1项，安徽省自然基金面上项目1项，省级自然科学研究重点项目1项、一般项目 1 项。发表学术论文 40 余篇。E-mail：gdwu1120@qq.com。;谢东辰，硕士研究生，主要研究方向为推荐系统。E-mail：764361338@qq.com。;黄雯婧，硕士研究生，主要研究方向为推荐系统。E-mail：799558789@qq.com。
通讯作者:吴国栋. E-mail：gdwu1120@qq.com

更新日期/Last Update: 1900-01-01

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检索增强生成推荐及其研究进展

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