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Online Learning to Rank: Absolute vs. Relative

Authors:

Yiwei Chen,

Katja HofmannAuthors Info & Claims

WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web

Pages 19 - 20

https://doi.org/10.1145/2740908.2742718

Published: 18 May 2015 Publication History

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Abstract

Online learning to rank holds great promise for learning personalized search result rankings. First algorithms have been proposed, namely absolute feedback approaches, based on contextual bandits learning; and relative feedback approaches, based on gradient methods and inferred preferences between complete result rankings. Both types of approaches have shown promise, but they have not previously been compared to each other. It is therefore unclear which type of approach is the most suitable for which online learning to rank problems. In this work we present the first empirical comparison of absolute and relative online learning to rank approaches.

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Cited By

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Yu WLi STang XWang K(2019)An efficient top-k ranking method for service selection based on ?-ADMOPSO algorithmNeural Computing and Applications10.1007/s00521-018-3640-931:1(77-92)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1007/s00521-018-3640-9
Rahimi RShakery AKando NSakai TJoho HLi Hde Vries AWhite R(2017)Online Learning to Rank for Cross-Language Information RetrievalProceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3077136.3080710(1033-1036)Online publication date: 7-Aug-2017
https://dl.acm.org/doi/10.1145/3077136.3080710
Li JLiu GYan CJiang C(2017)Robust Learning to Rank Based on Portfolio Theory and AMOSA AlgorithmIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2016.258478647:6(1007-1018)Online publication date: Jun-2017
https://doi.org/10.1109/TSMC.2016.2584786

Index Terms

Online Learning to Rank: Absolute vs. Relative
1. Information systems
  1. Information retrieval

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Published In

WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web

May 2015

1602 pages

ISBN:9781450334730

DOI:10.1145/2740908

General Chairs:
Aldo Gangemi
National Research Council, Italy & Paris 13 University-CNRS, France
,
Stefano Leonardi
Sapienza University of Rome, Italy
,
Alessandro Panconesi
Sapienza University of Rome, Italy

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

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Published: 18 May 2015

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WWW '15

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WWW '15: 24th International World Wide Web Conference

May 18 - 22, 2015

Florence, Italy

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Yu WLi STang XWang K(2019)An efficient top-k ranking method for service selection based on ?-ADMOPSO algorithmNeural Computing and Applications10.1007/s00521-018-3640-931:1(77-92)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1007/s00521-018-3640-9
Rahimi RShakery AKando NSakai TJoho HLi Hde Vries AWhite R(2017)Online Learning to Rank for Cross-Language Information RetrievalProceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3077136.3080710(1033-1036)Online publication date: 7-Aug-2017
https://dl.acm.org/doi/10.1145/3077136.3080710
Li JLiu GYan CJiang C(2017)Robust Learning to Rank Based on Portfolio Theory and AMOSA AlgorithmIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2016.258478647:6(1007-1018)Online publication date: Jun-2017
https://doi.org/10.1109/TSMC.2016.2584786

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Online learning to rank with top-k feedback

Multileave Gradient Descent for Fast Online Learning to Rank

Unbiased Learning to Rank: Online or Offline?

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