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:最近的新闻建议工作表明,建议者可以过度地向用户展示支持其先前存在的意见的文章。然而,大多数现有工作侧重于静态设置或短时窗口,留下关于新闻建议的长期和动态影响的开放问题。 在本文中,我们通过系统研究三个研究问题来探讨这些动态影响:(1) 在与建议者反复进行长期互动之后,用户的新闻阅读行为如何改变?(2) 在这种动态建议系统里,用户的固有偏好如何随时间而改变?(3) 现有的SOTA静态方法能否缓解动态环境中的问题?具体地说,我们通过在40 000篇附加说明的新闻报道基础上对新闻建议的政治两极分化进行模拟实验,进行一项全面的数据驱动研究。我们发现,随着建议者的发展,用户迅速接触到更为极端的内容。 我们还发现,基于校准的干预可以减缓这种对立的两极分化现象,但为今后的改进留有重要机会。