[1]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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Retrieval-augmented generation for recommendation and research progress

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 3 Page number: 577-597 Column: 综述 Public date: 2026-05-05
Title:
Retrieval-augmented generation for recommendation and research progress
Author(s):
WU Guodong;  XIE Dongchen;  HUANG Wenjing;  ZHENG Yang;  TU Lijing
School of Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China
Keywords:
RAG; recommendation; LLM; retrieval; external knowledge; deep learning; knowledge base; representation learning
CLC:
TP301
DOI:
10.11992/tis.202508007
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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