Nowadays, research into personalization has been focusing on explainability and fairness. Several approaches proposed in recent works are able to explain individual recommendations in a post-hoc manner or by explanation paths. However, explainability techniques applied to unfairness in recommendation have been limited to finding user/item features mostly related to biased recommendations. In this paper, we devised a novel algorithm that leverages counterfactuality methods to discover user unfairness explanations in the form of user-item interactions. In our counterfactual framework, interactions are represented as edges in a bipartite graph, with users and items as nodes. Our bipartite graph explainer perturbs the topological structure to find an altered version that minimizes the disparity in utility between the protected and unprotected demographic groups. Experiments on four real-world graphs coming from various domains showed that our method can systematically explain user unfairness on three state-of-the-art GNN-based recommendation models. Moreover, an empirical evaluation of the perturbed network uncovered relevant patterns that justify the nature of the unfairness discovered by the generated explanations. The source code and the preprocessed data sets are available at https://github.com/jackmedda/RS-BGExplainer.

翻译：如今，个性化研究日益关注可解释性和公平性。近年提出的多种方法能够通过事后解释或解释路径对个体推荐进行说明。然而，应用于推荐不公问题的可解释性技术仍局限于识别与偏差推荐最相关的用户/物品特征。本文设计了一种新颖算法，利用反事实方法以用户-物品交互的形式发现用户不公解释。在我们的反事实框架中，交互被表示为二分图中的边，用户和物品作为节点。该二分图解释器通过扰动拓扑结构，寻找使受保护群体与非受保护群体之间效用差异最小化的修正版本。在来自不同领域的四个真实图数据集上的实验表明，我们的方法能够系统性地解释三种基于GNN的最先进推荐模型中的用户不公现象。此外，对扰动网络的经验评估揭示了相关模式，这些模式印证了生成解释所发现的不公本质。源代码与预处理数据集可在https://github.com/jackmedda/RS-BGExplainer获取。