论文标题
推荐中的受欢迎程度偏见，校准和公平性之间的联系
The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation
论文作者
论文摘要
最近，人们对公平感知的推荐系统越来越感兴趣，包括在提供不同用户或用户组的稳定性能方面的公平性。如果建议不公平地代表某些用户的口味，而其他组收到与他们的偏好一致的建议，则推荐系统可能会被视为不公平。在本文中，我们使用称为错误校准的度量标准来衡量建议算法如何响应用户的真实偏好，并考虑各种算法如何导致不同程度的不同用户的误解程度。特别是，我们猜测，推荐中众所周知的现象的普及偏差是导致推荐中误解的一个重要因素。我们使用两个现实世界数据集的实验结果表明，不同用户群体如何受算法流行偏见的影响与他们对流行项目的兴趣水平之间存在联系。此外，我们表明，一个群体受算法流行偏见的影响越多，他们的建议就越被校准了。
Recently there has been a growing interest in fairness-aware recommender systems including fairness in providing consistent performance across different users or groups of users. A recommender system could be considered unfair if the recommendations do not fairly represent the tastes of a certain group of users while other groups receive recommendations that are consistent with their preferences. In this paper, we use a metric called miscalibration for measuring how a recommendation algorithm is responsive to users' true preferences and we consider how various algorithms may result in different degrees of miscalibration for different users. In particular, we conjecture that popularity bias which is a well-known phenomenon in recommendation is one important factor leading to miscalibration in recommendation. Our experimental results using two real-world datasets show that there is a connection between how different user groups are affected by algorithmic popularity bias and their level of interest in popular items. Moreover, we show that the more a group is affected by the algorithmic popularity bias, the more their recommendations are miscalibrated.
