Abstract

Recent advancements in deep neural networks for graph-structured data have\nled to state-of-the-art performance on recommender system benchmarks. However,\nmaking these methods practical and scalable to web-scale recommendation tasks\nwith billions of items and hundreds of millions of users remains a challenge.\nHere we describe a large-scale deep recommendation engine that we developed and\ndeployed at Pinterest. We develop a data-efficient Graph Convolutional Network\n(GCN) algorithm PinSage, which combines efficient random walks and graph\nconvolutions to generate embeddings of nodes (i.e., items) that incorporate\nboth graph structure as well as node feature information. Compared to prior GCN\napproaches, we develop a novel method based on highly efficient random walks to\nstructure the convolutions and design a novel training strategy that relies on\nharder-and-harder training examples to improve robustness and convergence of\nthe model. We also develop an efficient MapReduce model inference algorithm to\ngenerate embeddings using a trained model. We deploy PinSage at Pinterest and\ntrain it on 7.5 billion examples on a graph with 3 billion nodes representing\npins and boards, and 18 billion edges. According to offline metrics, user\nstudies and A/B tests, PinSage generates higher-quality recommendations than\ncomparable deep learning and graph-based alternatives. To our knowledge, this\nis the largest application of deep graph embeddings to date and paves the way\nfor a new generation of web-scale recommender systems based on graph\nconvolutional architectures.\n

Affiliated Institutions

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Publication Info

Year
2018
Type
article
Pages
974-983
Citations
2562
Access
Closed

Social Impact

Social media, news, blog, policy document mentions

Citation Metrics

2562
OpenAlex
215
Influential

Cite This

Rex Ying, Ruining He, Kaifeng Chen et al. (2018). Graph Convolutional Neural Networks for Web-Scale Recommender Systems. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 974-983. https://doi.org/10.1145/3219819.3219890

Identifiers

DOI
10.1145/3219819.3219890
arXiv
1806.01973

Data Quality

Data completeness: 79%