揭开深度过滤泡：短视频推荐中的窄曝光现象
Uncovering the Deep Filter Bubble: Narrow Exposure in Short-Video Recommendation
AI总结：
本研究基于中国头部短视频平台的一年交互数据，定义并探究了短视频推荐中的深度过滤泡现象，分析了其演变规律与影响因素，并提出了降低用户陷入过滤泡风险的推荐系统设计方向。
摘要：
过滤泡因其可能导致用户不满或极化等不良后果，已在在线内容平台语境下被广泛研究。随着短视频平台的兴起，过滤泡受到了额外关注，因为这类平台前所未有地依赖推荐系统来提供相关内容。在本研究中，我们探究了深度过滤泡——即用户在自身广泛兴趣范围内接触到的内容十分狭窄的现象。我们使用中国某头部短视频平台的一年交互数据完成了这项研究，该数据包含每支视频三级分类的层级数据。我们在此语境下正式定义了“深度”过滤泡，随后探究了数据中的多种关联：首先了解深度过滤泡随时间的演变规律，接着揭示了引发该现象的部分因素，例如特定内容分类、用户人口统计学特征以及反馈类型。我们观察到，尽管处于过滤泡中的用户总体比例随时间基本保持稳定，但其过滤泡的深度构成会发生变化。此外，我们发现部分人口统计学群体接触到更窄内容的概率更高，而隐式反馈信号能够减少过滤泡的形成。最后，我们提出了若干设计推荐系统的方法，以降低用户陷入过滤泡的风险。
英文摘要：
Filter bubbles have been studied extensively within the context of online content platforms due to their potential to cause undesirable outcomes such as user dissatisfaction or polarization. With the rise of short-video platforms, the filter bubble has been given extra attention because these platforms rely on an unprecedented use of the recommender system to provide relevant content. In our work, we investigate the deep filter bubble, which refers to the user being exposed to narrow content within their broad interests. We accomplish this using one-year interaction data from a top short-video platform in China, which includes hierarchical data with three levels of categories for each video. We formalize our definition of a "deep" filter bubble within this context, and then explore various correlations within the data: first understanding the evolution of the deep filter bubble over time, and later revealing some of the factors that give rise to this phenomenon, such as specific categories, user demographics, and feedback type. We observe that while the overall proportion of users in a filter bubble remains largely constant over time, the depth composition of their filter bubble changes. In addition, we find that some demographic groups that have a higher likelihood of seeing narrower content and implicit feedback signals can lead to less bubble formation. Finally, we propose some ways in which recommender systems can be designed to reduce the risk of a user getting caught in a bubble.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
