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C. Burges", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Adapting Boosting for Information Retrieval Measures", "url": "https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/LambdaMART_Final.pdf" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Krysta M. Svore", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Adapting Boosting for Information Retrieval Measures", "url": "https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/LambdaMART_Final.pdf" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Jianfeng Gao", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Adapting Boosting for Information Retrieval Measures", "url": "https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/LambdaMART_Final.pdf" } } ], "evidence": [ { "description": "Bibliographic/stated-result source; not a WRS proof verification", "id": "source-0", "kind": "source_reference", "source": { "citation": "Adapting Boosting for Information Retrieval Measures", "url": "https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/LambdaMART_Final.pdf" } }, { "description": "Bibliographic/stated-result source; not a WRS proof verification", "id": "source-1", "kind": "source_reference", "source": { "citation": "Adapting boosting for information retrieval measures — publication history", "url": "https://link.springer.com/article/10.1007/s10791-009-9112-1" } } ], "origin": "HUMAN_LEGACY", "record_profile": "wrs-local-research-notes-0.1", "record_type": "research_note", "rrp_version": "0.1", "scope": { "description": "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.", "domain": "Learning to rank" }, "sources": [ { "citation": "Adapting Boosting for Information Retrieval Measures", "url": "https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/LambdaMART_Final.pdf" }, { "citation": "Adapting boosting for information retrieval measures — publication history", "url": "https://link.springer.com/article/10.1007/s10791-009-9112-1" } ], "statement": "# LambdaMART/LambdaSMART: metric-aware boosted trees and ranker adaptation\n\nFitted 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.\n\n## Scientific scope\n\nExact 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.\n\n## Reading status\n\nThis 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.\n\n## People and systems\n\nQiang Wu; Christopher J. C. Burges; Krysta M. Svore; Jianfeng Gao\n\nSource-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.\n\n\n\n## Original sources\n\n- [Adapting Boosting for Information Retrieval Measures](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/LambdaMART_Final.pdf)\n- [Adapting boosting for information retrieval measures — publication history](https://link.springer.com/article/10.1007/s10791-009-9112-1)\n", "summary": "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.", "title": "LambdaMART/LambdaSMART: metric-aware boosted trees and ranker adaptation" }, "record_id": "urn:uuid:c033b818-a4f7-46d8-ae17-9f625cc893fd", "version": 1, "source_ref": { "record_id": "urn:uuid:c033b818-a4f7-46d8-ae17-9f625cc893fd", "version": 1, "content_hash": "sha256:aa2637f9dfdd757369c5e5e7e776f4f883eeb894dfe7d7ecb3546dff8ce3c3b7" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:45f8eaaae537715b18bcaab584cfd11284a1e98ec6a0df4c99452f9c2a2e7f93" }, "target": { "record_id": "urn:uuid:c033b818-a4f7-46d8-ae17-9f625cc893fd", "version": 1, "content_hash": "sha256:aa2637f9dfdd757369c5e5e7e776f4f883eeb894dfe7d7ecb3546dff8ce3c3b7" }, "published_grade": { "profile": "wrs-published-grade-1", "edition": "2026-10-28.1", "id": "wu-et-al-lambdamart-2009", "entry_id": "wu-et-al-lambdamart-2009", "revision": 1, "title": "LambdaMART/LambdaSMART: metric-aware boosted trees and ranker adaptation", "context": "Learning to rank", "track": "Human-designed research method or resource", "band": "A", "modifier": "+", "status": "PROVISIONAL_EDITORIAL", "assessed_on": "2026-10-16", "evidence_cutoff": "2026-10-16", "achievement_state": "SUPPORTED_SCOPED_RESULT", "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.", "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_summary": "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.", "references": [ { "title": "Adapting Boosting for Information Retrieval Measures", "url": "https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/LambdaMART_Final.pdf", "locator": "Original contribution and stated assumptions, evaluation scope and limitations; source-linked assessment, not independent execution or proof certification." }, { "title": "Adapting boosting for information retrieval measures — publication history", "url": "https://link.springer.com/article/10.1007/s10791-009-9112-1", "locator": "Original contribution and stated assumptions, evaluation scope and limitations; source-linked assessment, not independent execution or proof certification." } ], "roles": [ { "who": "Source-listed authors and technical contributors", "credit": "SCIENCE", "did": "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." } ], "target": { "record_id": "urn:uuid:c033b818-a4f7-46d8-ae17-9f625cc893fd", "version": 1, "content_hash": "sha256:aa2637f9dfdd757369c5e5e7e776f4f883eeb894dfe7d7ecb3546dff8ce3c3b7" }, "scientific_correctness_certified": false, "independent_expert_calibration": false, "method": "PROPRIETARY", "points": "165000" }, "canonical_agent_submission": false, "scientific_verification_awarded": false, "reader_guide": null, "reading_status": "SOURCE_LINKED_NOTE" }