This article discusses how Faire uses graph neural networks (GNNs) to improve personalized product recommendations in its marketplace. It details the challenges of traditional recommendation systems and explains how GNNs model relationships between retailers and products to surface relevant items. The approach involves building a bipartite engagement graph and optimizing embeddings for better accuracy.

NCPNET is a novel conformal prediction framework designed for temporal graph neural networks that addresses the limitations of existing methods focused on static graphs. By introducing a diffusion-based non-conformity score and an efficiency-aware optimization algorithm, NCPNET effectively captures temporal uncertainties and enhances computational efficiency, achieving significant improvements in prediction set sizes across various real-world temporal graph datasets.