In recent years, graph neural networks (GNNs) have become a popular tool to improve the accuracy and performance of recommender systems. Modern recommender systems are not only designed to serve end users, but also to benefit other participants, such as items and items providers. These participants may have different or conflicting goals and interests, which raise the need for fairness and popularity bias considerations. GNN-based recommendation methods also face the challenges of unfairness and popularity bias and their normalization and aggregation processes suffer from these challenges. In this paper, we propose a fair GNN-based recommender system, called HetroFair, to improve items' side fairness. HetroFair uses two separate components to generate fairness-aware embeddings: i) fairnessaware attention which incorporates dot product in the normalization process of GNNs, to decrease the effect of nodes' degrees, and ii) heterophily feature weighting to assign distinct weights to different features during the aggregation process. In order to evaluate the effectiveness of HetroFair, we conduct extensive experiments over six real-world datasets. Our experimental results reveal that HetroFair not only alleviates the unfairness and popularity bias on items' side, but also achieves superior accuracy on users' side. Our implementation is publicly available at https://github.com/NematGH/HetroFair.

翻译：近年来，图神经网络已成为提升推荐系统准确性与性能的流行工具。现代推荐系统不仅服务于终端用户，还需兼顾其他参与者（如商品及其供应商）的利益。这些参与者可能具有不同甚至冲突的目标与诉求，从而引发对公平性与流行度偏差问题的关注。基于图神经网络的推荐方法同样面临公平性与流行度偏差的挑战，其归一化与聚合过程亦受此影响。本文提出一种基于图神经网络的公平推荐系统HetroFair，以提升商品侧的公平性。该系统通过两个独立组件生成公平感知嵌入：i）在GNN归一化过程中引入点积运算的公平感知注意力机制，以降低节点度的影响；ii）异配特征加权机制，在聚合过程中为不同特征分配差异化权重。为评估HetroFair的有效性，我们在六个真实数据集上进行了广泛实验。结果表明，HetroFair不仅能缓解商品侧的不公平性与流行度偏差，同时在用户侧实现了更优的推荐精度。代码已开源：https://github.com/NematGH/HetroFair。