AWhat changed
Introduced explicit bounded-degree cross layers alongside deep networks, later extending vector cross parameters to richer matrix, low-rank and mixture-of-expert cross structures.
Scientific scope
Introduced explicit bounded-degree cross layers alongside deep networks, later extending vector cross parameters to richer matrix, low-rank and mixture-of-expert cross structures. 2017DCN and2020DCN-M(later DCNv2 title) kept as one source-specific family. Original vector cross coefficients are constrained, not arbitrary independent polynomial coefficients. Enhanced expressivity/empirical business metrics are not global optimization, universal ranking gains or causal user-benefit guarantees; inherited MoE/low-rank methods retain credit.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. 2017DCN and2020DCN-M(later DCNv2 title) kept as one source-specific family. Original vector cross coefficients are constrained, not arbitrary independent polynomial coefficients. Enhanced expressivity/empirical business metrics are not global optimization, universal ranking gains or causal user-benefit guarantees; inherited MoE/low-rank methods retain credit.
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.
- Deep & Cross Network for Ad Click Predictions
- DCN-M: Improved Deep & Cross Network for Feature Cross Learning in Web-scale Learning to Rank 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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