Ranking learning-to-rank methods

D. Hiemstra, N. Tax, S. Bockting

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

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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

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