Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations

TL;DR

This paper systematically evaluates 8 consumer fairness mitigation procedures in recommender systems, analyzing their impact on utility, fairness notions, and demographic groups, providing a comprehensive comparison and highlighting future challenges.

Contribution

It offers a unified evaluation of multiple fairness mitigation methods in recommender systems using a common protocol and datasets, addressing heterogeneity in prior assessments.

Findings

Mitigation procedures vary in their impact on recommendation utility.

Fairness notions based on equity and independence can conflict.

Certain demographic groups are disproportionately harmed by some mitigation methods.

Abstract

Enabling non-discrimination for end-users of recommender systems by introducing consumer fairness is a key problem, widely studied in both academia and industry. Current research has led to a variety of notions, metrics, and unfairness mitigation procedures. The evaluation of each procedure has been heterogeneous and limited to a mere comparison with models not accounting for fairness. It is hence hard to contextualize the impact of each mitigation procedure w.r.t. the others. In this paper, we conduct a systematic analysis of mitigation procedures against consumer unfairness in rating prediction and top-n recommendation tasks. To this end, we collected 15 procedures proposed in recent top-tier conferences and journals. Only 8 of them could be reproduced. Under a common evaluation protocol, based on two public data sets, we then studied the extent to which recommendation utility and consumer fairness are impacted by these procedures, the interplay between two primary fairness notions based on equity and independence, and the demographic groups harmed by the disparate impact. Our study finally highlights open challenges and future directions in this field. The source code is available at https://github.com/jackmedda/C-Fairness-RecSys.