Recommender systems are technology-driven marketing solutions for businesses that analyze user behavior data. However, collaborative data sharing between enterprises is often prohibited by privacy protection regulations, leading to insufficient data for graph neural networks (GNNs) training. Fortunately, federated learning (FL), a collaborative training framework without exposing source data, can be applied congruently. Nevertheless, most of FL-based GNN model training methods adopt federated averaging, which performs poorly on highly heterogeneous graph data. To solve this problem, a FL-based GNN Model Training framework for cross-enterprise recommendation, named FL-GMT, is proposed. Specifically, a GNN-based recommendation model is deployed as the local training model. Then, considering the performance inequity caused by uneven sample quality, a loss-based federated aggregation algorithm is designed, effectively improving the performance of disadvantaged participants. To improve the system stability at the end of the aggregation, a dynamic update method of loss attention is designed. Extensive experiments on benchmark datasets demonstrate that FL-GMT outperforms baselines in terms of system fairness, stability, and accuracy.

中文翻译：

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使用图神经网络的基于联合学习的跨企业推荐

推荐系统是技术驱动的营销解决方案，适用于分析用户行为数据的企业。然而，企业之间的协作数据共享往往被隐私保护法规所禁止，导致图神经网络（GNN）训练的数据不足。幸运的是，可以一致地应用联邦学习 (FL)，一种无需暴露源数据的协作训练框架。然而，大多数基于 FL 的 GNN 模型训练方法都采用联邦平均，这在高度异构的图数据上表现不佳。为了解决这个问题，提出了一种基于FL的跨企业推荐的GNN模型训练框架FL-GMT。具体来说，基于 GNN 的推荐模型被部署为本地训练模型。然后，考虑到样本质量不均导致的性能不公平，设计了一种基于损失的联合聚合算法，有效提高了弱势参与者的表现。为了提高聚合结束时的系统稳定性，设计了一种损失注意力的动态更新方法。对基准数据集的大量实验表明，FL-GMT 在系统公平性、稳定性和准确性方面优于基线。