Fairness in recommender systems: Evaluation approaches and assurance strategies

Yao WU, Jian CAO, Guandong XU

Research output: Contribution to journalArticlespeer-review

2 Citations (Scopus)

Abstract

With the wide application of recommender systems, the potential impacts of recommender systems on customers, item providers and other parties have attracted increasing attention. Fairness, which is the quality of treating people equally, is also becoming important in recommender system evaluation and algorithm design. Therefore, in the past years, there has been a growing interest in fairness measurement and assurance in recommender systems. Although there are several reviews on related topics, such as fairness in machine learning and debias in recommender systems, they do not present a systematic view on fairness in recommender systems, which is context aware and has a multi-sided meaning. Therefore, in this review, the concept of fairness is discussed in detail in the various contexts of recommender systems. Specifically, a comprehensive framework to classify fairness metrics is proposed from four dimensions, i.e., Fairness for Whom, Demographic Unit, Time Frame, and Quantification Method. Then the strategies for eliminating unfairness in recommendations, fairness in different recommendation tasks and datasets are reviewed and summarized. Finally, the challenges and future work are discussed. Copyright © 2023 held by the owner/author(s). Publication rights licensed to ACM.

Original languageEnglish
Article number10
JournalACM Transactions on Knowledge Discovery from Data
Volume18
Issue number1
DOIs
Publication statusPublished - Aug 2023

Citation

Wu, Y., Cao, J., & Xu, G. (2023). Fairness in recommender systems: Evaluation approaches and assurance strategies. ACM Transactions on Knowledge Discovery from Data, 18(1), Article 10. https://doi.org/10.1145/3604558

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