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DSSM: click-trained query/document semantic projections

Contribution grade

Neural semantic retrieval · Human-designed research method or resource

Reference score135,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Mapped query and document-title letter-n-gram features through deep nonlinear projections and trained cosine-similarity ranking from clicked-versus-sampled document pairs.

Scientific scope

Mapped query and document-title letter-n-gram features through deep nonlinear projections and trained cosine-similarity ranking from clicked-versus-sampled document pairs. Inherits latent semantic retrieval and discriminative click models; word hashing has collisions and click supervision is a relevance proxy, not semantic truth. Original evaluation ranks document titles, not guaranteed full-document understanding.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Inherits latent semantic retrieval and discriminative click models; word hashing has collisions and click supervision is a relevance proxy, not semantic truth. Original evaluation ranks document titles, not guaranteed full-document understanding.

Contributors

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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