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Directly optimizing evaluation measures in learning to rank based on the clonal selection algorithm

Published: 26 October 2010 Publication History

Abstract

One fundamental issue of learning to rank is the choice of loss function to be optimized. Although the evaluation measures used in Information Retrieval (IR) are ideal ones, in many cases they can't be used directly because they do not satisfy the smooth property needed in conventional machine learning algorithms. In this paper a new method named RankCSA is proposed, which tries to use IR evaluation measure directly. It employs the clonal selection algorithm to learn an effective ranking function by combining various evidences in IR. Experimental results on the LETOR benchmarh datasets demonstrate that RankCSA outperforms the baseline methods in terms of P@n, MAP and NDCG@n.

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Cited By

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  • (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
  • (2016)Research on B Cell Algorithm for Learning to Rank Method Based on Parallel StrategyPLOS ONE10.1371/journal.pone.015799411:8(e0157994)Online publication date: 3-Aug-2016
  • (2015)A cross-benchmark comparison of 87 learning to rank methodsInformation Processing and Management: an International Journal10.1016/j.ipm.2015.07.00251:6(757-772)Online publication date: 1-Nov-2015

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  1. Directly optimizing evaluation measures in learning to rank based on the clonal selection algorithm

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    cover image ACM Conferences
    CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management
    October 2010
    2036 pages
    ISBN:9781450300995
    DOI:10.1145/1871437
    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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    Publication History

    Published: 26 October 2010

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    Author Tags

    1. clonal selection algorithm
    2. information retrieval
    3. learning to rank
    4. machine learning
    5. ranking function

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    • (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
    • (2016)Research on B Cell Algorithm for Learning to Rank Method Based on Parallel StrategyPLOS ONE10.1371/journal.pone.015799411:8(e0157994)Online publication date: 3-Aug-2016
    • (2015)A cross-benchmark comparison of 87 learning to rank methodsInformation Processing and Management: an International Journal10.1016/j.ipm.2015.07.00251:6(757-772)Online publication date: 1-Nov-2015

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