  The advent of large language models marks a revolutionary breakthrough in
artificial intelligence. With the unprecedented scale of training and model
parameters, the capability of large language models has been dramatically
improved, leading to human-like performances in understanding, language
synthesizing, and common-sense reasoning, etc. Such a major leap-forward in
general AI capacity will change the pattern of how personalization is
conducted. For one thing, it will reform the way of interaction between humans
and personalization systems. Instead of being a passive medium of information
filtering, large language models present the foundation for active user
engagement. On top of such a new foundation, user requests can be proactively
explored, and user's required information can be delivered in a natural and
explainable way. For another thing, it will also considerably expand the scope
of personalization, making it grow from the sole function of collecting
personalized information to the compound function of providing personalized
services. By leveraging large language models as general-purpose interface, the
personalization systems may compile user requests into plans, calls the
functions of external tools to execute the plans, and integrate the tools'
outputs to complete the end-to-end personalization tasks. Today, large language
models are still being developed, whereas the application in personalization is
largely unexplored. Therefore, we consider it to be the right time to review
the challenges in personalization and the opportunities to address them with
LLMs. In particular, we dedicate this perspective paper to the discussion of
the following aspects: the development and challenges for the existing
personalization system, the newly emerged capabilities of large language
models, and the potential ways of making use of large language models for
personalization.
