Fairness in recommender systems has been considered with respect to sensitive
attributes of users (e.g., gender, race) or items (e.g., revenue in a
multistakeholder setting). Regardless, the concept has been commonly
interpreted as some form of equality -- i.e., the degree to which the system is
meeting the information needs of all its users in an equal sense. In this
paper, we argue that fairness in recommender systems does not necessarily imply
equality, but instead it should consider a distribution of resources based on
merits and needs.
We present a probabilistic framework based on generalized cross entropy to
evaluate fairness of recommender systems under this perspective, where we show
that the proposed framework is flexible and explanatory by allowing to
incorporate domain knowledge (through an ideal fair distribution) that can help
to understand which item or user aspects a recommendation algorithm is over- or
under-representing. Results on two real-world datasets show the merits of the
proposed evaluation framework both in terms of user and item fairness.

本文讨论推荐系统中的公平性，提出了一种基于概率的框架来评估推荐系统的公平性，该框架基于推荐算法偏向或低估哪些项目或用户方面的领域知识，并考虑到推荐资源如何基于优点和需求分配，研究结果表明所提出的评估框架在用户和项目公平性方面具有优点。