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.

翻译：过滤气泡因其可能导致用户不满或极化等不良后果，已在在线内容平台的背景下得到广泛研究。随着短视频平台的兴起，过滤气泡问题受到额外关注，因为这些平台前所未有地依赖推荐系统来提供相关内容。本研究探讨了深度过滤气泡，即用户在其广泛兴趣范围内被暴露于窄范围内容的现象。我们利用中国某顶级短视频平台一年的交互数据（包含每个视频三个层级类别的层次化数据）开展研究。在此背景下，我们正式定义了“深度”过滤气泡的概念，并探索数据中的各种相关性：首先理解深度过滤气泡随时间演化的规律，进而揭示导致该现象的因素，例如特定类别、用户人口统计特征及反馈类型。我们发现，虽然处于过滤气泡中的用户总体比例随时间大致保持恒定，但其过滤气泡的深度构成却在变化。此外，部分人口统计群体更有可能看到更窄范围的内容，而隐式反馈信号可能导致气泡形成减少。最后，我们提出了若干设计推荐系统以降低用户陷入气泡风险的方法。