Recommender systems are typically biased toward a small group of users, leading to severe unfairness in recommendation performance, i.e., User-Oriented Fairness (UOF) issue. The existing research on UOF is limited and fails to deal with the root cause of the UOF issue: the learning process between advantaged and disadvantaged users is unfair. To tackle this issue, we propose an In-processing User Constrained Dominant Sets (In-UCDS) framework, which is a general framework that can be applied to any backbone recommendation model to achieve user-oriented fairness. We split In-UCDS into two stages, i.e., the UCDS modeling stage and the in-processing training stage. In the UCDS modeling stage, for each disadvantaged user, we extract a constrained dominant set (a user cluster) containing some advantaged users that are similar to it. In the in-processing training stage, we move the representations of disadvantaged users closer to their corresponding cluster by calculating a fairness loss. By combining the fairness loss with the original backbone model loss, we address the UOF issue and maintain the overall recommendation performance simultaneously. Comprehensive experiments on three real-world datasets demonstrate that In-UCDS outperforms the state-of-the-art methods, leading to a fairer model with better overall recommendation performance.

翻译：推荐系统通常偏向于少数用户群体，导致推荐性能出现严重的不公平性问题，即用户导向公平性（UOF）问题。现有关于UOF的研究存在局限性，未能解决UOF问题的根本原因：优势用户与弱势用户之间的学习过程不公平。为解决该问题，我们提出了一种处理型用户约束主导集（In-UCDS）框架。这是一个通用框架，可应用于任何骨干推荐模型以实现用户导向公平性。我们将In-UCDS分为两个阶段：UCDS建模阶段和处理训练阶段。在UCDS建模阶段，我们为每个弱势用户提取一个约束主导集（用户聚类），其中包含与其相似的部分优势用户。在处理训练阶段，通过计算公平性损失，将弱势用户的表征向其对应聚类靠近。通过将公平性损失与原始骨干模型损失相结合，我们同时解决了UOF问题并保持了整体推荐性能。在三个真实数据集上的综合实验表明，In-UCDS优于现有最先进方法，能够在获得更公平模型的同时保持更好的整体推荐性能。