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越来越多的关注。根据涉及的利益相关方，RS的公平性可分为用户公平性、项目公平性以及同时考虑用户和项目公平性的两面公平性。然而，我们认为，即使RS实现了两面公平，仍然可能存在交叉两面不公平性——本文通过真实数据上的实证研究观察到并展示了这一现象，而此前对此尚未进行充分研究。为解决该问题，我们提出了一种名为“交叉两面公平推荐”（ITFR）的新方法。该方法利用锐度感知损失来识别弱势群体，随后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，我们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和分析表明，我们提出的方法有效缓解了交叉两面不公平性，且始终优于现有的最先进方法。