论文标题
推荐中的多面暴露偏见
Multi-sided Exposure Bias in Recommendation
论文作者
论文摘要
推荐系统的学术研究一直集中在与推荐的准确性衡量标准上。即使研究了非准确性措施，例如流行性偏见，多样性和新颖性，也通常仅从用户的角度来看。但是，许多实际推荐人通常是多方利益相关者的环境，在建议过程中应解决几个利益相关者的需求和利益。在本文中，我们专注于流行性偏见问题，这是许多推荐算法的众所周知的属性，在这些算法中很少有流行物品被过度推荐，而其他大多数项目都没有得到比例的关注并解决了其对不同利益相关者的影响。使用多种建议算法和两个在音乐和电影域中公开可用的数据集，我们从经验上展示了算法的固有流行性偏见，以及这种偏见如何影响不同的利益相关者，例如这些项目的用户和供应商。我们还建议指标从不同的利益相关者的角度衡量建议算法的暴露偏见。
Academic research in recommender systems has been greatly focusing on the accuracy-related measures of recommendations. Even when non-accuracy measures such as popularity bias, diversity, and novelty are studied, it is often solely from the users' perspective. However, many real-world recommenders are often multi-stakeholder environments in which the needs and interests of several stakeholders should be addressed in the recommendation process. In this paper, we focus on the popularity bias problem which is a well-known property of many recommendation algorithms where few popular items are over-recommended while the majority of other items do not get proportional attention and address its impact on different stakeholders. Using several recommendation algorithms and two publicly available datasets in music and movie domains, we empirically show the inherent popularity bias of the algorithms and how this bias impacts different stakeholders such as users and suppliers of the items. We also propose metrics to measure the exposure bias of recommendation algorithms from the perspective of different stakeholders.
