In recommendation literature, explainability and fairness are becoming two prominent perspectives to consider. However, prior works have mostly addressed them separately, for instance by explaining to consumers why a certain item was recommended or mitigating disparate impacts in recommendation utility. None of them has leveraged explainability techniques to inform unfairness mitigation. In this paper, we propose an approach that relies on counterfactual explanations to augment the set of user-item interactions, such that using them while inferring recommendations leads to fairer outcomes. Modeling user-item interactions as a bipartite graph, our approach augments the latter by identifying new user-item edges that not only can explain the original unfairness by design, but can also mitigate it. Experiments on two public data sets show that our approach effectively leads to a better trade-off between fairness and recommendation utility compared with state-of-the-art mitigation procedures. We further analyze the characteristics of added edges to highlight key unfairness patterns. Source code available at https://github.com/jackmedda/RS-BGExplainer/tree/cikm2023.

翻译：在推荐系统研究中，可解释性与公平性正成为两个日益重要的视角。然而，现有工作大多分别处理这两个问题，例如向消费者解释推荐某项商品的原因，或减轻推荐效用中的差异化影响，尚未有研究利用可解释技术来指导不公平性缓解。本文提出一种基于反事实解释的方法，通过增强用户-物品交互集合，使得在推理推荐时采用这些交互能够产生更公平的结果。我们将用户-物品交互建模为二分图，通过识别新的用户-物品边来增强该图，这些边不仅能够从设计上解释原有的不公平性，还能缓解该问题。在两个公开数据集上的实验表明，与最先进的缓解方法相比，我们的方法在公平性与推荐效用之间取得了更优的权衡。我们进一步分析了新增边的特征，以突出关键的不公平性模式。源代码见 https://github.com/jackmedda/RS-BGExplainer/tree/cikm2023。