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GraphSage

Definition

GraphSage is an inductive graph-embedding method (Hamilton, Ying, Leskovec, Inductive Representation Learning on Large Graphs, arXiv:1706.02216, 2017). Unlike transductive methods (DeepWalk, node2vec) that require the full graph at training, GraphSage generates embeddings for previously-unseen nodes by sampling + aggregating features from a node's neighborhood — a property load-bearing for production recommender systems where new items arrive continuously.

Role in Pinterest's Home Feed Blender

GraphSage is Pinterest's primary mechanism for defining Pin-to-Pin similarity in the diversification layer (Source: sources/2026-04-07-pinterest-evolution-of-multi-objective-optimization-at-pinterest-home):

  • DPP era (2021) — GraphSage + categorical taxonomy were the sole similarity signals.
  • SSD era (Early 2025) — GraphSage remains in the SSD signal mix alongside visual + text embeddings, capturing "relatedness in the Pin graph, including co-engagement patterns and neighborhood similarity."

GraphSage at Pinterest predates this post (Pinterest published its foundational PinSage paper at KDD 2018, extending GraphSage with random-walk-based sampling for web-scale recommenders — not ingested here).

Caveats

  • Stub — this page covers GraphSage's role as a Pin-similarity signal in the 2026-04-07 MOO post; the academic paper's full method + the PinSage Pinterest-specific extension are not summarised here.
  • Canonical Pinterest variant (PinSage) is referenced but not a separate wiki page at this time.
  • Exact GraphSage version used in Pinterest Home Feed Blender is not specified in the post.
  • Embedding dimension, training cadence, refresh latency not disclosed.

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