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Large Language Model Enhanced Recommender Systems: Methods, Applications and Trends
Qidong Liu (City University of Hong Kong, Xi'an Jiaotong University), Xiangyu Zhao (City University of Hong Kong), Yuhao Wang (City University of Hong Kong), Yejing Wang (City University of Hong Kong), Zijian Zhang (Jilin University), Yuqi Sun (Xi'an Jiaotong University), Xiang Li (Nanyang Technological University), Maolin Wang (City University of Hong Kong), Pengyue Jia (City University of Hong Kong), Chong Chen (Tsinghua University), Wei Huang, Feng Tian (Xi'an Jiaotong University)
Abstract: 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.