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        "description": "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.",
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      "statement": "# PinSage: random-walk sampled graph convolutions at recommendation scale\n\nCombined 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.\n\n## Scientific scope\n\nBuilds 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.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nRex Ying; Ruining He; Kaifeng Chen; Pong Eksombatchai; William L. Hamilton; Jure Leskovec\n\nSource-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.\n\n\n\n## Original sources\n\n- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://arxiv.org/abs/1806.01973v1)\n",
      "summary": "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.",
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