Recommenders with a Mission: Assessing Diversity in News Recommendations
Recommenders with a Mission: Assessing Diversity in News Recommendations
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有使命的推荐者：评估新闻推荐的多样性
DOI：
10.1145/3406522.3446019
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
N. Helberger
中科院分区：
文献类型：
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作者：
Sanne Vrijenhoek;Mesut Kaya;N. Metoui;J. Möller;Daan Odijk;N. Helberger
新闻推荐器帮助用户找到相关的在线内容，并有可能在民主社会中发挥关键作用，将公民稀缺的注意力引导到对他们来说最重要的信息上。与此同时，最近对所谓的过滤泡沫、错误信息和选择性曝光的担忧也体现了这些数字新闻推荐系统的颠覆性潜力。推荐系统可以制造或打破过滤气泡，因此可以有助于创建更封闭或更开放的互联网。当前评估推荐系统的方法通常侧重于衡量用户点击和短期参与度的增加，而不是衡量用户对各种重要信息的长期兴趣。本文旨在弥合植根于民主理论的多样性规范概念与评估推荐系统所需的定量指标之间的差距。我们提出了一套基于社会科学对多样性的解释的指标，并提出了实际实施的方法。
News recommenders help users to find relevant online content and have the potential to fulfill a crucial role in a democratic society, directing the scarce attention of citizens towards the information that is most important to them. Simultaneously, recent concerns about so-called filter bubbles, misinformation and selective exposure are symptomatic of the disruptive potential of these digital news recommenders. Recommender systems can make or break filter bubbles, and as such can be instrumental in creating either a more closed or a more open internet. Current approaches to evaluating recommender systems are often focused on measuring an increase in user clicks and short-term engagement, rather than measuring the user's longer term interest in diverse and important information. This paper aims to bridge the gap between normative notions of diversity, rooted in democratic theory, and quantitative metrics necessary for evaluating the recommender system. We propose a set of metrics grounded in social science interpretations of diversity and suggest ways for practical implementations.