User Modeling in the Era of Large Language Models: Current Research and Future Directions

TL;DR

This paper reviews how large language models are transforming user modeling by enhancing personalization and understanding of user-generated content, discusses current methods, applications, challenges, and future research directions.

Contribution

It provides a comprehensive overview of LLM-based user modeling approaches, categorizes existing techniques, and highlights future challenges and opportunities in the field.

Findings

LLMs significantly improve user understanding and personalization.

Integration of LLMs with text and graph methods enhances user modeling.

Future research should address challenges like data privacy and model interpretability.

Abstract

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