Analyzing and Mitigating Asymmetric Learning for Product Cold-Start in E-Commerce Purchase Prediction with Graph Neural Networks: Similarity-Driven History Augmentation

Graph Neural Networks (GNNs) have become a foundational tool for e-commerce recommendation systems, yet they consistently fail in zero-shot cold-start scenarios where new products enter the market without prior interactions. In this paper, we diagnose this failure as a structural vulnerability rather than a simple data sparsity issue. We introduce the concept of asymmetric learning, demonstrating that in severely imbalanced bipartite graphs, minority-type nodes (products) become disproportionately reliant on topological signals. By evaluating this phenomenon alongside a relatively balanced control dataset, we confirm that this performance collapse is a byproduct of the data structure rather than architectural design. To mitigate this limitation, we propose Similarity-Driven History Augmentation (SHA), a data-centric approach that assigns synthetic interaction histories to cold-start products by matching them with semantically similar established donors. To prevent these synthetic signals from degrading the representations of established nodes, we further introduce a decoupled hybrid framework alongside an enhanced SHA strategy that selectively filters active customers. Comprehensive evaluations across multiple real-world e-commerce datasets, including DataCo and Amazon Gift Cards, confirm the effectiveness and stability of our approach across different GNN architectures.