Explaining Recommendation Fairness from a User/Item Perspective

Recommender systems play a crucial role in personalizing user experiences, yet ensuring fairness in their outcomes remains an elusive challenge. This work explores the impact of individual users or items on the fairness of recommender systems, thus addressing a significant knowledge gap in the field. We introduce an innovative approach called Adding-based Counterfactual Fairness Reasoning (ACFR), designed to elucidate recommendation fairness from the unique perspectives of users and items. Conventional methodologies, like erasing-based counterfactual analysis, pose limitations, particularly in modern recommender systems dealing with a large number of users and items. These traditional methods, by excluding specific users or items, risk disrupting the crucial relational structure central to collaborative filtering recommendations. In contrast, ACFR employs an adding-based counterfactual analysis, a unique strategy allowing us to consider potential, yet-to-happen user-item interactions. This strategy preserves the core user-item relational structure, while predicting future behaviors of users or items. The commonly-used feature-based counterfactual analysis, relying on gradient-based optimization to identify interference on each feature, is not directly applicable in our case. In the recommendation scenario we consider, only interactions between users and items are present during model training—no distinct features are involved. Consequently, the traditional mechanism proves impractical for identifying interference on these existing interactions. Our extensive experiments validate the superiority of ACFR over traditional baseline methods, demonstrating significant improvements in recommendation fairness on benchmark datasets. This work, therefore, provides a fresh perspective and a promising methodology for enhancing fairness in recommender systems.