Regret-aware Re-ranking for Guaranteeing Two-sided Fairness and Accuracy in Recommender Systems

2025-04-20

AI Summary

To address the lack of individual fairness and the difficulty in jointly ensuring user accuracy and provider fairness in multi-stakeholder recommendation systems, this paper pioneers the integration of regret theory into fairness modeling. We propose a nonlinear regret function to formally capture users’ subjective perceptions of individual fairness. Building upon this, we design a Regret-Aware Fuzzy Programming Re-ranking Framework that jointly optimizes user-side accuracy and provider-side fairness. Our framework extends the BankFair paradigm to support multi-objective optimization with explicit trade-offs among competing stakeholders. Extensive experiments on two real-world datasets demonstrate that our method significantly outperforms state-of-the-art baselines across all three core metrics—individual fairness, user recommendation accuracy, and provider fairness—achieving synergistic improvement of all three objectives.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Data Mining & Knowledge Management: Recommender Systems
• Game Theory and Economic Paradigms: Fair Division

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

Abstract

In multi-stakeholder recommender systems (RS), users and providers operate as two crucial and interdependent roles, whose interests must be well-balanced. Prior research, including our work BankFair, has demonstrated the importance of guaranteeing both provider fairness and user accuracy to meet their interests. However, when they balance the two objectives, another critical factor emerges in RS: 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, in this paper, we propose our method BankFair+. Specifically, BankFair+ 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 user and provider, therefore balancing the trade-off 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.

Problem

• Balancing user accuracy and provider fairness in recommender systems
• Addressing individual fairness disparity in recommendation accuracy
• Ensuring trade-off between individual and provider fairness

Innovation

• Non-linear regret theory ensures individual fairness
• Regret-aware fuzzy programming balances trade-offs
• Two-step re-ranking enhances accuracy and fairness