In recent years, personalization research has been delving into issues of explainability and fairness. While some techniques have emerged to provide post-hoc and self-explanatory individual recommendations, there is still a lack of methods aimed at uncovering unfairness in recommendation systems beyond identifying biased user and item features. This paper proposes a new algorithm, GNNUERS, which uses counterfactuals to pinpoint user unfairness explanations in terms of user-item interactions within a bi-partite graph. By perturbing the graph topology, GNNUERS reduces differences in utility between protected and unprotected demographic groups. The paper evaluates the approach using four real-world graphs from different domains and demonstrates its ability to systematically explain user unfairness in three state-of-the-art GNN-based recommendation models. This perturbed network analysis reveals insightful patterns that confirm the nature of the unfairness underlying the explanations. The source code and preprocessed datasets are available at https://github.com/jackmedda/RS-BGExplainer

翻译：近年来，个性化研究深入探讨了可解释性与公平性问题。尽管已有技术能够提供后验及自解释性的个体推荐，但针对推荐系统中不公平性根源的揭示方法（除识别有偏用户与物品特征外）仍显不足。本文提出一种新算法GNNUERS，该算法利用反事实推理，在二部图框架中精准定位由用户-物品交互导致的用户不公平性解释。通过扰动图拓扑结构，GNNUERS能够缩减受保护与非受保护人口群体间的效用差异。本文采用四个跨领域真实世界图数据评估该方法，并证明其可在三种最先进的基于图神经网络的推荐模型中系统性解释用户不公平性。该扰动网络分析揭示了蕴含在解释背后不公平性本质的洞察性模式。源代码与预处理数据集已开源发布于https://github.com/jackmedda/RS-BGExplainer