揭开深度滤镜的泡沫:短视频推荐中的窄曝光率

ArXiv Pub Date : 2024-03-07 DOI:10.1145/3589334.3648159
Nicholas Sukiennik, Chen Gao, Nian Li
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引用次数: 0

摘要

由于过滤泡沫有可能导致用户不满或两极分化等不良后果,因此在网络内容平台中对过滤泡沫进行了广泛的研究。随着短视频平台的兴起,过滤泡沫受到了格外关注,因为这些平台前所未有地依赖推荐系统来提供相关内容。在我们的工作中,我们研究了深度过滤泡沫,它指的是用户在其广泛兴趣范围内接触到的狭窄内容。我们使用中国顶级短视频平台一年的交互数据来完成这一研究,这些数据包括每个视频的三级分类的分层数据。在此背景下,我们正式确定了 "深度 "过滤泡沫的定义,然后探索了数据中的各种相关性:首先了解了深度过滤泡沫随时间的演变,随后揭示了导致这一现象的一些因素,如特定类别、用户人口统计学和反馈类型。我们观察到,虽然过滤泡沫中用户的总体比例随着时间的推移基本保持不变,但其过滤泡沫的深度构成却在发生变化。此外,我们还发现,一些人口群体更有可能看到较窄的内容和隐含的反馈信号,这可能会导致较少的气泡形成。最后,我们提出了一些设计推荐系统的方法,以降低用户陷入泡沫的风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Uncovering the Deep Filter Bubble: Narrow Exposure in Short-Video Recommendation
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.
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