Recent work in news recommendation has demonstrated that recommenders can over-expose users to articles that support their pre-existing opinions. However, most existing work focuses on a static setting or over a short-time window, leaving open questions about the long-term and dynamic impacts of news recommendations. In this paper, we explore these dynamic impacts through a systematic study of three research questions: 1) How do the news reading behaviors of users change after repeated long-term interactions with recommenders? 2) How do the inherent preferences of users change over time in such a dynamic recommender system? 3) Can the existing SOTA static method alleviate the problem in the dynamic environment? Concretely, we conduct a comprehensive data-driven study through simulation experiments of political polarization in news recommendations based on 40,000 annotated news articles. We find that users are rapidly exposed to more extreme content as the recommender evolves. We also find that a calibration-based intervention can slow down this polarization, but leaves open significant opportunities for future improvements

翻译：近年来，新闻推荐领域的研究表明，推荐系统可能过度向用户推送支持其既有观点的文章。然而，现有研究大多聚焦静态环境或短时间窗口，对新闻推荐长期动态效应的问题仍缺乏探讨。本文通过系统性地研究三个科学问题来探索这些动态效应：1）在长期重复与推荐系统交互后，用户的新闻阅读行为如何演变？2）在这种动态推荐系统中，用户固有偏好随时间如何变化？3）现有静态最优方法能否缓解动态环境中的问题？具体而言，我们基于40,000篇带标注新闻文章，通过政治极化仿真实验开展全面的数据驱动研究。研究发现，随着推荐系统的演化，用户会迅速接触到更极端的内容。同时，基于校准的干预措施可减缓极化进程，但未来仍有显著的改进空间。