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

• URL: http://arxiv.org/abs/2508.14059v1
• Date: Sun, 10 Aug 2025 02:12:04 GMT
• Title: Graph Neural Network for Product Recommendation on the Amazon Co-purchase Graph
• Authors: Mengyang Cao, Frank F. Yang, Yi Jin, Yijun Yan,
• Abstract summary: Graph Neural Networks (GNNs) have demonstrated significant potential by utilizing structural and semantic relationships through graph-based learning.<n>This study assessed the abilities of four GNN architectures, LightGCN, GraphSAGE, GAT, and PinSAGE, on the Amazon Product Co-purchase Network.
• Score: 3.855006051648698
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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