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.