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 source code is available at https://github.com/jackmedda/C-Fairness-RecSys.

arXiv commentAccepted at the 44th European Conference on Information Retrieval (ECIR 2022)

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Cited by in corpus (4)

• A Survey on Popularity Bias in Recommender Systems
• A Stakeholder-Centered View on Fairness in Music Recommender Systems
• Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation
• Unmasking Gender Bias in Recommendation Systems and Enhancing Category-Aware Fairness