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Article

Learning to rank using gradient descent

Authors:

Nicole Hamilton,

Greg HullenderAuthors Info & Claims

ICML '05: Proceedings of the 22nd international conference on Machine learning

Pages 89 - 96

https://doi.org/10.1145/1102351.1102363

Published: 07 August 2005 Publication History

Abstract

We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function, and we introduce RankNet, an implementation of these ideas using a neural network to model the underlying ranking function. We present test results on toy data and on data from a commercial internet search engine.

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Cited By

蒋彩(2025)Research on Optimization of Cross-Modal Retrieval Based on Ranking LossModeling and Simulation10.12677/mos.2025.14101214:01(116-121)Online publication date: 2025
https://doi.org/10.12677/mos.2025.141012
Sun FYin YLiu HShen LKang XXin GLiu LZheng J(2025)ROASMI: accelerating small molecule identification by repurposing retention dataJournal of Cheminformatics10.1186/s13321-025-00968-817:1Online publication date: 14-Feb-2025
https://doi.org/10.1186/s13321-025-00968-8
Li ZZhang CSong DNejdl WAuer SKarras OCha MMoens MNajork M(2025)Dynamic Interaction-Driven Intent Evolver with Semantic Probability DistributionsProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703508(290-299)Online publication date: 10-Mar-2025
https://dl.acm.org/doi/10.1145/3701551.3703508
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Published In

cover image ACM Other conferences

ICML '05: Proceedings of the 22nd international conference on Machine learning

August 2005

1113 pages

ISBN:1595931805

DOI:10.1145/1102351

General Chair:
Saso Dzeroski
Jozef Stefan Institute, Slovenia
,
Program Chairs:
Luc De Raedt,
Stefan Wrobel

Copyright © 2005 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 07 August 2005

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蒋彩(2025)Research on Optimization of Cross-Modal Retrieval Based on Ranking LossModeling and Simulation10.12677/mos.2025.14101214:01(116-121)Online publication date: 2025
https://doi.org/10.12677/mos.2025.141012
Sun FYin YLiu HShen LKang XXin GLiu LZheng J(2025)ROASMI: accelerating small molecule identification by repurposing retention dataJournal of Cheminformatics10.1186/s13321-025-00968-817:1Online publication date: 14-Feb-2025
https://doi.org/10.1186/s13321-025-00968-8
Li ZZhang CSong DNejdl WAuer SKarras OCha MMoens MNajork M(2025)Dynamic Interaction-Driven Intent Evolver with Semantic Probability DistributionsProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703508(290-299)Online publication date: 10-Mar-2025
https://dl.acm.org/doi/10.1145/3701551.3703508
Li PSun YJuan HChen CTsai HHuang J(2025)A large language model framework for literature-based disease–gene association predictionBriefings in Bioinformatics10.1093/bib/bbaf07026:1Online publication date: 25-Feb-2025
https://doi.org/10.1093/bib/bbaf070
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