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arXiv · 2508.14059

Graph Neural Network for Product Recommendation on the Amazon Co-purchase Graph

Mengyang Cao·Frank F. Yang·Yi Jin·Yijun Yan
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Abstract

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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Mengyang Cao, Frank F. Yang, Yi Jin, Yijun Yan. 2025-08-10. Graph Neural Network for Product Recommendation on the Amazon Co-purchase Graph. https://arxiv.org/abs/2508.14059

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