The many facets of fairness in recommender systems: Consumers, providers and items

Abstract

Autonomous decision-making systems, particularly recommender systems, have received increasing attention concerning fairness, i.e., if all stakeholders affected by such a system are treated equally as a result of the recommendations. Existing approaches primarily focus on fairness between two stakeholders – consumers and providers or consumers and items – treating providers and items as the same entity. However, we argue for the treatment of providers and items as distinct stakeholders to offer more comprehensive models of fairness in recommender systems. To this end, we propose a fairness-aware recommender system, CIPFRS, designed to optimize fairness across all three key stakeholders: consumers, providers, and items. We examine consumer fairness regarding their level of interaction with the system; high and low-activity users should be treated equally. Further, all providers should have an equal opportunity for their products to be recommended. Finally, we propose an approach to implement item fairness in each provider’s inventory. We report an extensive evaluation of the proposed solution through three datasets, demonstrating that considering all three stakeholders yields improved recommendations while minimizing bias.