TY - GEN
T1 - Large Language Model Enhanced Recommender Systems
T2 - 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2025
AU - Liu, Qidong
AU - Zhao, Xiangyu
AU - Wang, Yuhao
AU - Wang, Yejing
AU - Zhang, Zijian
AU - Sun, Yuqi
AU - Li, Xiang
AU - Wang, Maolin
AU - Jia, Pengyue
AU - Chen, Chong
AU - Huang, Wei
AU - Tian, Feng
N1 - Publisher Copyright:
© 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2025/8/3
Y1 - 2025/8/3
N2 - Due to exceptional reasoning and understanding abilities, the Large Language Model (LLM) has revolutionized the pattern of many fields, including recommender systems (RS). There has been a handful of research that focuses on empowering the RS by LLM. Recently, considering the latency and memory costs in real-world applications, LLM-Enhanced RS (LLMERS) is highlighted. This direction pushes the LLM into the online system with a large step by eliminating the utilization of LLM during inference. As a cutting-edge field, there is a clear need for a comprehensive survey to summarize this direction. In this survey, we systematically investigate the most up-to-date works of LLM-enhanced RS to boost this direction. Based on the component of an RS model that the LLM aims to augment, the basic taxonomy includes Knowledge Enhancement, Interaction Enhancement and Model Enhancement. Additionally, we identify several promising research directions. To facilitate access to the surveyed papers, we release a repository.
AB - Due to exceptional reasoning and understanding abilities, the Large Language Model (LLM) has revolutionized the pattern of many fields, including recommender systems (RS). There has been a handful of research that focuses on empowering the RS by LLM. Recently, considering the latency and memory costs in real-world applications, LLM-Enhanced RS (LLMERS) is highlighted. This direction pushes the LLM into the online system with a large step by eliminating the utilization of LLM during inference. As a cutting-edge field, there is a clear need for a comprehensive survey to summarize this direction. In this survey, we systematically investigate the most up-to-date works of LLM-enhanced RS to boost this direction. Based on the component of an RS model that the LLM aims to augment, the basic taxonomy includes Knowledge Enhancement, Interaction Enhancement and Model Enhancement. Additionally, we identify several promising research directions. To facilitate access to the surveyed papers, we release a repository.
KW - Enhancement
KW - Large Language Model
KW - Recommender Systems
UR - https://www.scopus.com/pages/publications/105014313464
U2 - 10.1145/3711896.3736553
DO - 10.1145/3711896.3736553
M3 - 会议稿件
AN - SCOPUS:105014313464
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 6096
EP - 6106
BT - KDD 2025 - Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
Y2 - 3 August 2025 through 7 August 2025
ER -