Ranking learning-to-rank methods

D. Hiemstra, N. Tax, S. Bockting

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    132 Downloads (Pure)

    Abstract

    We present a cross-benchmark comparison of learning-to-rank methods using two evaluation measures: the Normalized Winning Number and the Ideal Winning Number. Evaluation results of 87 learning-to-rank methods on 20 datasets show that ListNet, SmoothRank, FenchelRank, FSMRank, LRUF and LARF are Pareto optimal learning-to-rank methods, listed in increasing order of Normalized Winning Number and decreasing order of Ideal Winning Number.

    Original languageEnglish
    Title of host publicationProceedings of the 1st International Workshop on LEARning Next gEneration Rankers
    EditorsN. Ferro, C. Lucchese, M. Maistro, R. Perego
    Place of PublicationAachen
    Pages3-3
    Number of pages1
    Publication statusPublished - 27 Nov 2017
    Event1st International Workshop on LEARning Next gEneration Rankers (LEARNER 2017), October 1, 2017, Amsterdam, Netherlands - Amsterdam, Netherlands
    Duration: 1 Oct 20171 Oct 2017
    http://learner2017.dei.unipd.it/

    Publication series

    NameCEUR Workshop Proceedings
    Volume2007
    ISSN (Print)1613-0073

    Workshop

    Workshop1st International Workshop on LEARning Next gEneration Rankers (LEARNER 2017), October 1, 2017, Amsterdam, Netherlands
    Abbreviated titleLEARNER 2017
    Country/TerritoryNetherlands
    CityAmsterdam
    Period1/10/171/10/17
    Internet address

    Fingerprint

    Dive into the research topics of 'Ranking learning-to-rank methods'. Together they form a unique fingerprint.

    Cite this