PinSage: random-walk sampled graph convolutions at recommendation scale

What changed

Combined visit-count-weighted neighborhood sampling, shared local graph convolutions, CPU–GPU minibatch production and distributed embedding inference to train and serve recommendation embeddings on a large pin–board graph.

Scientific scope

Combined visit-count-weighted neighborhood sampling, shared local graph convolutions, CPU–GPU minibatch production and distributed embedding inference to train and serve recommendation embeddings on a large pin–board graph. Builds on GraphSAGE and prior graph convolutions rather than inventing graph neural learning. Sampled neighborhoods trade coverage for bounded work; performance and engagement lifts are source-reported for selected Pinterest tasks, not a guarantee for arbitrary graphs or an independently replayed deployment.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Builds on GraphSAGE and prior graph convolutions rather than inventing graph neural learning. Sampled neighborhoods trade coverage for bounded work; performance and engagement lifts are source-reported for selected Pinterest tasks, not a guarantee for arbitrary graphs or an independently replayed deployment.

Contributors

• Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

• Graph Convolutional Neural Networks for Web-Scale Recommender Systems

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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