In the current landscape of ever-increasing levels of digitalization, we are
facing major challenges pertaining to scalability. Recommender systems have
become irreplaceable both for helping users navigate the increasing amounts of
data and, conversely, aiding providers in marketing products to interested
users. The growing awareness of discrimination in machine learning methods has
recently motivated both academia and industry to research how fairness can be
ensured in recommender systems. For recommender systems, such issues are well
exemplified by occupation recommendation, where biases in historical data may
lead to recommender systems relating one gender to lower wages or to the
propagation of stereotypes. In particular, consumer-side fairness, which
focuses on mitigating discrimination experienced by users of recommender
systems, has seen a vast number of diverse approaches for addressing different
types of discrimination. The nature of said discrimination depends on the
setting and the applied fairness interpretation, of which there are many
variations. This survey serves as a systematic overview and discussion of the
current research on consumer-side fairness in recommender systems. To that end,
a novel taxonomy based on high-level fairness interpretation is proposed and
used to categorize the research and their proposed fairness evaluation metrics.
Finally, we highlight some suggestions for the future direction of the field.

本文为乘用侧公平在推荐系统中的研究提供了系统的概览和讨论，并提出了一种基于高级公平解释的新型分类方法，用于对研究及其提出的公平评价指标进行分类，最后提出了该领域未来的一些方向建议。