比较四种图神经网络在亚马逊商品共购图上的推荐效果。
Graph Neural Network for Product Recommendation on the Amazon Co-purchase Graph
- 用图神经网络挖掘商品共购关系中的结构与语义信息
- LightGCN在准确率和训练速度上表现最佳，优于其他模型
- 适合需要高效部署的电商推荐系统开发者参考
在海量数据中识别相关资讯是现代推荐系统面临的挑战。图神经网络（GNN）通过基于图的学习，利用结构和语义关系展现出显著潜力。本研究在亚马逊商品共购网络上，评估了四种GNN架构（LightGCN、GraphSAGE、GAT、PinSAGE）在链接预测任务下的性能。分析了各模型在精度、可扩展性、训练复杂度和泛化能力之间的实际权衡。结果揭示了不同模型在真实推荐场景中部署时的表现特征。
原文摘要 · Abstract (English)
Identifying relevant information among massive volumes of data is a challenge for modern recommendation systems. Graph Neural Networks (GNNs) have demonstrated significant potential by utilizing structural and semantic relationships through graph-based learning. This study assessed the abilities of four GNN architectures, LightGCN, GraphSAGE, GAT, and PinSAGE, on the Amazon Product Co-purchase Network under link prediction settings. We examined practical trade-offs between architectures, model performance, scalability, training complexity and generalization. The outcomes demonstrated each model's performance characteristics for deploying GNN in real-world recommendation scenarios.
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