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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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Digital Library

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Information

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

New York, NY, United States

Publication History

Published: 07 August 2005

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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
He SXiao YMing AMa H(2025)Prompt-guided image color aesthetics assessment: Models, datasets and benchmarksInformation Fusion10.1016/j.inffus.2024.102706114(102706)Online publication date: Feb-2025
https://doi.org/10.1016/j.inffus.2024.102706
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https://doi.org/10.1007/s00521-024-10828-5
Selwon KSzymański J(2024)Enhancing Personalized Travel Recommendations: Integrating User Behavior and Content AnalysisProceedings of the 32nd International Conference on Information Systems Development10.62036/ISD.2024.49Online publication date: 2024
https://doi.org/10.62036/ISD.2024.49
Ding ZChen SJia RChen YKiyavash NMooij J(2024)Learning to rank for active learning via multi-task bilevel optimizationProceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence10.5555/3702676.3702727(1112-1128)Online publication date: 15-Jul-2024
https://dl.acm.org/doi/10.5555/3702676.3702727
Wang JDong PJin YZhan RZhou ZSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Adaptively learning to select-rank in online platformsProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694128(50288-50312)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3694128
Tian CShi ZQin XLi LXu CSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Ranking-based client imitation selection for efficient federated learningProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694040(48211-48225)Online publication date: 21-Jul-2024
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Du XXiu LTanaka-Ishii KSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Bottleneck-minimal indexing for generative document retrievalProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692542(11888-11904)Online publication date: 21-Jul-2024
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