对YouTube上兔子洞现象的建模
Modeling Rabbit-Holes on YouTube
AI总结：
本文提出一种以用户侧机器人为中心的大规模数据收集方法和简单理论，通过解析建模严格定义了YouTube上的兔子洞现象，为推荐系统的大规模自动化审计铺平道路。
摘要：
许多讨论都指出社交媒体上存在所谓的兔子洞（RH）现象，这些平台致力于为用户提供高级的个性化服务。这种现象被宽泛地理解为主流推荐的崩塌，取而代之的是将用户锁定在狭窄且专业化信息流中的超个性化推荐。然而，定量研究往往忽略个性化，规模有限，且依赖人工标记来追踪这种崩塌。这阻碍了基于可复现观察来精确理解该现象，从而无法对平台进行持续审计。本文首先通过提出一种以用户侧机器人（bot）为中心的方法来解决规模问题，该方法通过对推荐进行自动播放游走，实现了大规模数据收集。随后，我们提出了一个简单的理论来解释这些RH的出现。尽管该理论是对这一复杂的全球性现象的简化视角，但它具有多重优势：它可以被解析建模，并为RH提供了一个通用且严格的定义。我们将其定义为i）用户与个性化的交互，以及ii）某些视频类别的吸引力强度之间的相互作用，这些因素导致用户迅速偏离针对新用户配置文件的主流推荐。我们通过展示在YouTube上收集超过1600万条个性化推荐后发现的RH来说明这些概念。最后的验证步骤将我们自动识别的RH与先前研究工作中手动识别的RH进行了比较。这些结果共同为推荐系统中RH效应的大规模自动化审计铺平了道路。
英文摘要：
Numerous discussions have advocated the presence of a so called rabbit-hole (RH) phenomenon on social media, interested in advanced personalization to their users. This phenomenon is loosely understood as a collapse of mainstream recommendations, in favor of ultra personalized ones that lock users into narrow and specialized feeds. Yet quantitative studies are often ignoring personalization, are of limited scale, and rely on manual tagging to track this collapse. This precludes a precise understanding of the phenomenon based on reproducible observations, and thus the continuous audits of platforms. In this paper, we first tackle the scale issue by proposing a user-sided bot-centric approach that enables large scale data collection, through autoplay walks on recommendations. We then propose a simple theory that explains the appearance of these RHs. While this theory is a simplifying viewpoint on a complex and planet-wide phenomenon, it carries multiple advantages: it can be analytically modeled, and provides a general yet rigorous definition of RHs. We define them as an interplay between i) user interaction with personalization and ii) the attraction strength of certain video categories, which cause users to quickly step apart of mainstream recommendations made to fresh user profiles. We illustrate these concepts by highlighting some RHs found after collecting more than 16 million personalized recommendations on YouTube. A final validation step compares our automatically-identified RHs against manually-identified RHs from a previous research work. Together, those results pave the way for large scale and automated audits of the RH effect in recommendation systems.
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