A Survey on the Fairness of Recommender Systems

By Yifan Wang, Weizhi Ma, M. Zhang, Yiqun Liu, Shaoping Ma. ACM Trans. Inf. Syst., 2022

456 citations recorded by the source corpus.

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.

Subfields: Computer Science Applications, Information Systems, Marketing

View Paper: https://www.semanticscholar.org/paper/63a9d47f42d1d1e8bd8fb1183b88ca82297c37bb

Citations in the corpus

• Fairness in Machine Learning: A Survey (2020, unknown): “For a general overview and taxonomy of fairness concepts, we refer to the surveys for fairness in information retrieval and recommender systems [92, 294] and fairness in ranking [311, 312].”
• Recommender Systems in the Era of Large Language Models (LLMs) (2023, unknown): “......”