A Survey on the Fairness of Recommender Systems

TL;DR

This survey reviews over 60 papers on fairness in recommender systems, summarizing definitions, datasets, measurements, and methods, and discusses future research directions to address ethical and social concerns.

Contribution

It provides a comprehensive taxonomy and classification of fairness issues, datasets, and methods in recommender systems, filling a gap in scattered research.

Findings

Classifies fairness definitions and issues in recommendation.

Summarizes datasets and measurement techniques.

Provides a taxonomy of fairness methods.

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.