Fairness of recommender systems (RS) has attracted increasing attention recently. Based on the involved stakeholders, the fairness of RS can be divided into user fairness, item fairness, and two-sided fairness which considers both user and item fairness simultaneously. However, we argue that the intersectional two-sided unfairness may still exist even if the RS is two-sided fair, which is observed and shown by empirical studies on real-world data in this paper, and has not been well-studied previously. To mitigate this problem, we propose a novel approach called Intersectional Two-sided Fairness Recommendation (ITFR). Our method utilizes a sharpness-aware loss to perceive disadvantaged groups, and then uses collaborative loss balance to develop consistent distinguishing abilities for different intersectional groups. Additionally, predicted score normalization is leveraged to align positive predicted scores to fairly treat positives in different intersectional groups. Extensive experiments and analyses on three public datasets show that our proposed approach effectively alleviates the intersectional two-sided unfairness and consistently outperforms previous state-of-the-art methods.

翻译：推荐系统（RS）的公平性近年来受到广泛关注。根据涉及的利益相关者，RS的公平性可分为用户公平性、物品公平性以及同时兼顾用户与物品公平性的双向公平性。然而，本文通过真实数据上的实证研究表明，即使推荐系统实现了双向公平，交叉双向不公平现象仍可能存在，且此前尚未得到充分研究。为缓解该问题，我们提出一种名为交叉双向公平推荐（ITFR）的新方法。该方法利用锐度感知损失来识别劣势群体，并通过协同损失平衡为不同交叉群体建立一致的区分能力。此外，采用预测分数归一化来对齐正向预测分数，以公平对待不同交叉群体中的正向样本。在三个公开数据集上的大量实验与分析表明，所提方法有效缓解了交叉双向不公平问题，且性能持续优于现有最优方法。