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Tubes & Bubbles -- Topological confinement of YouTube recommendations
arXiv - CS - Computers and Society Pub Date : 2020-01-15 , DOI: arxiv-2001.05324
Camille Roth and Antoine Mazi\`eres and Telmo Menezes

The role of recommendation algorithms in online user confinement is at the heart of a fast-growing literature. Recent empirical studies generally suggest that filter bubbles may principally be observed in the case of explicit recommendation (based on user-declared preferences) rather than implicit recommendation (based on user activity). We focus on YouTube which has become a major online content provider but where confinement has until now been little-studied in a systematic manner. Starting from a diverse number of seed videos, we first describe the properties of the sets of suggested videos in order to design a sound exploration protocol able to capture latent recommendation graphs recursively induced by these suggestions. These graphs form the background of potential user navigations along non-personalized recommendations. From there, be it in topological, topical or temporal terms, we show that the landscape of what we call mean-field YouTube recommendations is often prone to confinement dynamics. Moreover, the most confined recommendation graphs i.e., potential bubbles, seem to be organized around sets of videos that garner the highest audience and thus plausibly viewing time.

中文翻译:

Tubes & Bubbles -- YouTube 推荐的拓扑限制

推荐算法在在线用户限制中的作用是快速增长的文献的核心。最近的实证研究通常表明,过滤气泡主要在显式推荐(基于用户声明的偏好)而不是隐式推荐(基于用户活动)的情况下观察到。我们专注于 YouTube,它已成为主要的在线内容提供商,但迄今为止很少有人以系统的方式研究限制。从不同数量的种子视频开始,我们首先描述建议视频集的属性,以设计一个合理的探索协议,能够捕获由这些建议递归诱导的潜在推荐图。这些图形成了潜在用户导航的背景,沿着非个性化推荐。从那里,无论是在拓扑、主题还是时间方面,我们都表明我们所谓的平均场 YouTube 推荐的景观通常容易受到限制动态的影响。此外,最受限制的推荐图,即潜在的气泡,似乎是围绕获得最多观众的视频集组织的,因此似乎是观看时间。
更新日期:2020-04-27
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