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.
@article{arxiv.2508.14059,
title = {Graph Neural Network for Product Recommendation on the Amazon Co-purchase Graph},
author = {Mengyang Cao and Frank F. Yang and Yi Jin and Yijun Yan},
journal= {arXiv preprint arXiv:2508.14059},
year = {2025}
}