User Modeling in the Era of Large Language Models: Current Research and Future Directions
It addresses the problem of enhancing user modeling for applications such as recommendation and healthcare by summarizing existing LLM-based approaches, but it is incremental as it reviews and categorizes rather than introduces new methods.
This article reviews how large language models (LLMs) enhance user modeling by integrating with text and graph-based methods to improve personalization and detection in applications like recommendation and healthcare, noting their superior performance in handling user-generated content.
User modeling (UM) aims to discover patterns or learn representations from user data about the characteristics of a specific user, such as profile, preference, and personality. The user models enable personalization and suspiciousness detection in many online applications such as recommendation, education, and healthcare. Two common types of user data are text and graph, as the data usually contain a large amount of user-generated content (UGC) and online interactions. The research of text and graph mining is developing rapidly, contributing many notable solutions in the past two decades. Recently, large language models (LLMs) have shown superior performance on generating, understanding, and even reasoning over text data. The approaches of user modeling have been equipped with LLMs and soon become outstanding. This article summarizes existing research about how and why LLMs are great tools of modeling and understanding UGC. Then it reviews a few categories of large language models for user modeling (LLM-UM) approaches that integrate the LLMs with text and graph-based methods in different ways. Then it introduces specific LLM-UM techniques for a variety of UM applications. Finally, it presents remaining challenges and future directions in the LLM-UM research. We maintain the reading list at: https://github.com/TamSiuhin/LLM-UM-Reading