通过校准解决推荐中受欢迎程度偏差的多利益相关者影响
Addressing the Multistakeholder Impact of Popularity Bias in Recommendation Through Calibration
摘要：
流行度偏差是推荐系统中一个众所周知的现象：流行的项目被推荐的频率甚至超过了它们的流行度，放大了许多推荐领域中已经存在的长尾效应。先前的研究已经研究了各种方法来减轻流行偏见和增强整体长尾项目的推荐。然而，这些方法的有效性尚未在多利益攸关方环境中进行评估，在这种环境中，除了接受建议的用户之外，还应考虑所建议物品的供应商的效用。在本文中，我们提出了流行度校准的概念，它测量的用户配置文件中的项目和推荐项目的流行度分布之间的匹配。我们还开发了一个算法，优化这个指标。此外，我们表明，现有的评价指标的流行偏见不反映性能的算法时，它是从不同的利益相关者的角度来衡量。使用音乐和电影数据集，我们的经验表明，我们的方法优于现有的国家的最先进的方法，在解决流行的偏见，通过校准的建议，以用户的喜好。我们还表明，我们提出的算法有一个次要的影响，提高供应商的公平性。
英文摘要：
Popularity bias is a well-known phenomenon in recommender systems: popular items are recommended even more frequently than their popularity would warrant, amplifying long-tail effects already present in many recommendation domains. Prior research has examined various approaches for mitigating popularity bias and enhancing the recommendation of long-tail items overall. The effectiveness of these approaches, however, has not been assessed in multistakeholder environments where in addition to the users who receive the recommendations, the utility of the suppliers of the recommended items should also be considered. In this paper, we propose the concept of popularity calibration which measures the match between the popularity distribution of items in a user's profile and that of the recommended items. We also develop an algorithm that optimizes this metric. In addition, we demonstrate that existing evaluation metrics for popularity bias do not reflect the performance of the algorithms when it is measured from the perspective of different stakeholders. Using music and movie datasets, we empirically show that our approach outperforms the existing state-of-the-art approaches in addressing popularity bias by calibrating the recommendations to users' preferences. We also show that our proposed algorithm has a secondary effect of improving supplier fairness.
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