Large Language Model Enhanced Recommender Systems-Methods, Applications and Trends

Abstract

Due to the brilliant abilities in reasoning and understanding, the Large Language Model (LLM) has revolutionized the pattern of several fields, including recommender systems (RS). There have 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. However, it is a rather cutting-edge field, and a comprehensive tutorial is desirable to conclude this direction. In this tutorial, we 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. In each basic cluster, we identify and summarize what and how to augment the traditional RS. Besides, for further advancements in both industrial and academic fields, we give out its applications and several promising directions. A repository is released to facilitate access to the surveyed papers.

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If you have any questions, please contact us at liuqidong@stu.xjtu.edu.cn.