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LambdaMART/LambdaSMART: metric-aware boosted trees and ranker adaptation

Contribution grade

Learning to rank · Human-designed research method or resource

Reference score165,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Fitted boosted regression trees to LambdaRank update signals, supported warm-start adaptation from an existing ranker and enumerated rank-order crossing points for exact line search between two fixed rankers.

Scientific scope

Fitted boosted regression trees to LambdaRank update signals, supported warm-start adaptation from an existing ranker and enumerated rank-order crossing points for exact line search between two fixed rankers. Exact best mixing of two fixed rankers on finite labeled data is not global optimality over all boosted ensembles or guaranteed generalization. LambdaRank metric optimality is explicitly empirical. Online publication September3,2009 precedes the June2010 volume issue; year2009 follows primary publisher publication date.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Exact best mixing of two fixed rankers on finite labeled data is not global optimality over all boosted ensembles or guaranteed generalization. LambdaRank metric optimality is explicitly empirical. Online publication September3,2009 precedes the June2010 volume issue; year2009 follows primary publisher publication date.

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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