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 the 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) fairness-aware 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 the items' side, but also achieves superior accuracy on the 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，还需惠及物品及其提供者等其他参与者。这些参与者可能持有不同甚至相互冲突的目标与利益，从而引发对公平性与流行度偏差的关注。基于图神经网络的推荐方法同样面临不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。本文提出一种名为HetroFair的公平图神经网络推荐系统，旨在提升物品侧的公平性。HetroFair通过两个独立组件生成公平性感知嵌入：i) 公平性感知注意力机制，在GNN的归一化过程中引入点积运算以降低节点度数的影响；ii) 异质性特征加权机制，在聚合过程中为不同特征分配差异化权重。为验证HetroFair的有效性，我们在六个真实数据集上进行了广泛实验。实验结果表明，HetroFair不仅缓解了物品侧的不公平性与流行度偏差，同时在用户侧实现了卓越的准确性。我们的实现代码已在https://github.com/NematGH/HetroFair 公开提供。