  Recommendation systems usually recommend the existing contents to different
users. However, in comparison to static recommendation methods, a
recommendation logic that dynamically adjusts based on user interest
preferences may potentially attract a larger user base. Thus, we consider
paraphrasing existing content based on the interests of the users to modify the
content to better align with the preferences of users. In this paper, we
propose a new task named Target-Audience Oriented Content Paraphrase aims to
generate more customized contents for the target audience. We introduce the
task definition and the corresponding framework for the proposed task and the
creation of the corresponding datasets. We utilize the Large Language Models
(LLMs) and Large Vision Models (LVMs) to accomplish the base implementation of
the TOP framework and provide the referential baseline results for the proposed
task.
