Recommender systems are an essential tool to relieve the information overload
challenge and play an important role in people's daily lives. Since
recommendations involve allocations of social resources (e.g., job
recommendation), an important issue is whether recommendations are fair. Unfair
recommendations are not only unethical but also harm the long-term interests of
the recommender system itself. As a result, fairness issues in recommender
systems have recently attracted increasing attention. However, due to multiple
complex resource allocation processes and various fairness definitions, the
research on fairness in recommendation is scattered. To fill this gap, we
review over 60 papers published in top conferences/journals, including TOIS,
SIGIR, and WWW. First, we summarize fairness definitions in the recommendation
and provide several views to classify fairness issues. Then, we review
recommendation datasets and measurements in fairness studies and provide an
elaborate taxonomy of fairness methods in the recommendation. Finally, we
conclude this survey by outlining some promising future directions.

本文对超过 60 篇发表于顶级会议 / 期刊的论文进行了综述，探讨了在推荐系统中公正性的挑战以及该主题的定义和分类，同时对公正性的度量、公正方法以及未来研究方向进行了评述。