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MultiBiSage: bipartite-view fusion for heterogeneous recommendation graphs

Decomposed heterogeneous entity interactions into bipartite views, aggregated each view's sampled visual/textual neighborhood with a Transformer and fused the resulting view embeddings through another attention layer.

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Decomposed heterogeneous entity interactions into bipartite views, aggregated each view's sampled visual/textual neighborhood with a Transformer and fused the resulting view embeddings through another attention layer.

Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.

Scientific scope

A distinct heterogeneous-view fusion extension, not another count for PinSage deployment or its original sampler. Reuses Pixie and distributed data workflows. View decomposition, stored neighborhoods and selected losses constrain expressiveness and cost; v1's template DOI/venue placeholders are not exported as valid publication facts.

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People and systems

Saket Gurukar; Nikil Pancha; Andrew Zhai; Eric Kim; Samson Hu; Srinivasan Parthasarathy; Charles Rosenberg; Jure Leskovec

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.

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English catalogue narrative

MultiBiSage: bipartite-view fusion for heterogeneous recommendation graphs

Decomposed heterogeneous entity interactions into bipartite views, aggregated each view's sampled visual/textual neighborhood with a Transformer and fused the resulting view embeddings through another attention layer.

Scientific scope

A distinct heterogeneous-view fusion extension, not another count for PinSage deployment or its original sampler. Reuses Pixie and distributed data workflows. View decomposition, stored neighborhoods and selected losses constrain expressiveness and cost; v1's template DOI/venue placeholders are not exported as valid publication facts.

Reading status

This 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.

People and systems

Saket Gurukar; Nikil Pancha; Andrew Zhai; Eric Kim; Samson Hu; Srinivasan Parthasarathy; Charles Rosenberg; Jure Leskovec

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.

Original sources