Hybrid Graph Neural Networks
Authors: Weihua Hu, Matthias Fey
Hybrid GNNs: Transforming recommendation systems with Kumo AI
Recommendation systems have been a subject of great innovation over the past few decades, starting with matrix factorization in the early 2000s and evolving into two-tower and other deep learning approaches in the 2010s. In recent times, graph neural networks emerged as the leading approach to power recommender systems, enabling many well known tech companies (such as Pinterest, Uber, Amazon, and others) to deliver magical customer experiences and double-digit lifts in business metrics. Kumo offers a robust architecture known as hybrid graph neural networks (hybrid GNNs), which has been empirically shown to deliver outstanding performance on both public Kaggle data science challenges, and real-world production deployments.....