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arXiv · 2206.03761

A Survey on the Fairness of Recommender Systems

Yifan Wang·Weizhi Ma·Min Zhang·Yiqun Liu·Shaoping Ma
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Abstract

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.

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Yifan Wang, Weizhi Ma, Min Zhang, Yiqun Liu, Shaoping Ma. 2022-06-08. A Survey on the Fairness of Recommender Systems. https://doi.org/10.1145/3547333

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