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Parallel learning to rank for information retrieval

Published: 24 July 2011 Publication History

Abstract

Learning to rank represents a category of effective ranking methods for information retrieval. While the primary concern of existing research has been accuracy, learning efficiency is becoming an important issue due to the unprecedented availability of large-scale training data and the need for continuous update of ranking functions. In this paper, we investigate parallel learning to rank, targeting simultaneous improvement in accuracy and efficiency.

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  1. Parallel learning to rank for information retrieval

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    cover image ACM Conferences
    SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
    July 2011
    1374 pages
    ISBN:9781450307574
    DOI:10.1145/2009916

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 24 July 2011

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    Author Tags

    1. cooperative coevolution
    2. information retrieval
    3. learning to rank
    4. mapreduce
    5. parallel algorithms

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    • (2016)Learning to rankGenetic Programming and Evolvable Machines10.1007/s10710-016-9263-y17:3(203-230)Online publication date: 1-Sep-2016
    • (2015)FlexGPJournal of Grid Computing10.1007/s10723-014-9320-913:3(391-407)Online publication date: 1-Sep-2015
    • (2013)Cloud driven design of a distributed genetic programming platformProceedings of the 16th European conference on Applications of Evolutionary Computation10.1007/978-3-642-37192-9_51(509-518)Online publication date: 3-Apr-2013
    • (2012)Parallelizing ListNet training using sparkProceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval10.1145/2348283.2348502(1127-1128)Online publication date: 12-Aug-2012
    • (2012)Improving on-demand learning to rank through parallelismProceedings of the 13th international conference on Web Information Systems Engineering10.1007/978-3-642-35063-4_38(526-537)Online publication date: 28-Nov-2012
    • (2012)A library to run evolutionary algorithms in the cloud using mapreduceProceedings of the 2012t European conference on Applications of Evolutionary Computation10.1007/978-3-642-29178-4_42(416-425)Online publication date: 11-Apr-2012

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