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.

翻译：推荐系统通常将现有内容推荐给不同用户。然而，与静态推荐方法相比，能够根据用户兴趣偏好动态调整的推荐逻辑可能吸引更广泛的用户群体。因此，我们考虑基于用户兴趣对现有内容进行复述，以调整内容使其更符合用户偏好。本文提出一项名为"面向目标受众的内容复述"的新任务，旨在为目标受众生成更具定制化的内容。我们介绍了该任务的定义、相应框架以及对应数据集的构建方法。利用大语言模型（LLMs）和大视觉模型（LVMs），我们实现了TOP框架的基础版本，并为该任务提供了参考基线结果。