解开社会信号的影响

T. Hogg, Kristina Lerman
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引用次数: 29

摘要

同行推荐是一项众包任务,它利用许多人的意见来识别在线有趣的内容,如新闻、图像或视频。同行推荐应用通常使用社交信号,例如,先前推荐的数量,来引导人们到更有趣的内容。人们对社交信号的反应,结合内容质量及其呈现顺序,决定了同伴推荐的结果,即项目受欢迎程度。使用Amazon Mechanical Turk,我们实验测量了社交信号在同伴推荐中的影响。具体来说,在控制了项目内容和位置的变化后,我们发现社交信号对项目受欢迎程度的影响大约是位置和内容的一半。这些影响在某种程度上是相关的,所以社会信号加剧了“富人变得更富”的现象,这导致了受欢迎程度的更大差异。此外,社交信号会改变个人偏好,产生一种“羊群效应”,使人们对内容的判断产生偏见。尽管如此,我们发现社交信号在保持推荐质量的同时减少了评估内容的工作量,从而提高了同行推荐的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Disentangling the Effects of Social Signals
Peer recommendation is a crowdsourcing task that leverages the opinions of many to identify interesting content online, such as news, images, or videos. Peer recommendation applications often use social signals, e.g., the number of prior recommendations, to guide people to the more interesting content. How people react to social signals, in combination with content quality and its presentation order, determines the outcomes of peer recommendation, i.e., item popularity. Using Amazon Mechanical Turk, we experimentally measure the effects of social signals in peer recommendation. Specifically, after controlling for variation due to item content and its position, we find that social signals affect item popularity about half as much as position and content do. These effects are somewhat correlated, so social signals exacerbate the "rich get richer" phenomenon, which results in a wider variance of popularity. Further, social signals change individual preferences, creating a "herding" effect that biases people's judgments about the content. Despite this, we find that social signals improve the efficiency of peer recommendation by reducing the effort devoted to evaluating content while maintaining recommendation quality.
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