Graph Convolutional Neural Networks for Web-Scale Recommender Systems（用于Web级推荐系统的图形卷积神经网络）

Graph Convolutional Neural Networks for Web-Scale Recommender Systems

用于Web级推荐系统的图形卷积神经网络

ABSTRACT

Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. However, making these methods practical and scalable to web-scale recommendation tasks with billions of items and hundreds of millions of users remains a challenge.

Here we describe a large-scale deep recommendation engine that we developed and deployed at Pinterest. We develop a data-efficient Graph Convolutional Network (GCN) algorithm PinSage, which combines efficient random walks and graph convolutions to generate embeddings of nodes (i.e., items) that incorporate both graph structure as well as node feature information. Compared to prior GCN approaches, we develop a novel method