  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
