No Stakeholder Left Behind: Regret-Aware Re-Ranking for Two-Sided Fair Recommendation
Xiaopeng Ye, Chen Xu, Jun Xu, Xuyang Xie, Gang Wang, Zhenhua Dong · ACM Transactions on Information Systems · 2026
In multi-stakeholder recommender systems (RS), users and providers operate as two crucial and interdependent roles, whose interests must be well-balanced. Prior research has demonstrated the importance of guaranteeing both provider fairness and user accuracy to meet their interests. However, when balancing the two objectives, another critical factor emerges: individual fairness, which manifests as a significant disparity in individual recommendation accuracy, with some users receiving high accuracy while others are left with notably low accuracy. This oversight severely harms the interests of users and exacerbates social polarization. How to guarantee individual fairness while ensuring user accuracy and provider fairness remains an unsolved problem. To bridge this gap, this article proposes a method called BankFair+, which extends BankFair with two steps: (1) introducing a non-linear function from regret theory to ensure individual fairness while enhancing user accuracy; (2) formulating the re-ranking process as a regret-aware fuzzy programming problem to meet the interests of both individual users and providers, therefore balancing the tradeoff between individual fairness and provider fairness. Experiments on two real-world recommendation datasets demonstrate that BankFair+ outperforms all baselines regarding individual fairness, user accuracy, and provider fairness, indicating its ability to guarantee two-sided fairness and accuracy in RS.