Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations

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 Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation

“…Most papers in this field focus on the fairness perspective either on the side of users or items [16,5,2,20,3]. Contrary to these works, the work at hand aims to look into the interplay between accuracy, producer fairness, and consumer fairness in a multi-sided marketplace ecosystem and the possible trade-off between them.…”