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 (counterfactual explanation) 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，个性化研究聚焦于可解释性与公平性。近期工作提出的多种方法能够以事后方式或通过解释路径来解释个体推荐结果。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见推荐最相关的用户/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，寻找使受保护与非受保护人口群体间效用差异最小化的变异版本（反事实解释）。在四个来自不同领域的真实图上的实验表明，该方法能够系统性地解释基于三种最先进GNN推荐模型的用户不公现象。此外，对扰动网络的实证评估揭示了相关模式，证明了生成解释所发现的不公本质的合理性。源代码与预处理数据集可从https://github.com/jackmedda/RS-BGExplainer获取。