An unsupervised learning algorithm for rank aggregation

Alexandre Klementiev, Dan Roth, Kevin Small

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Many applications in information retrieval, natural language processing, data mining, and related fields require a ranking of instances with respect to a specified criteria as opposed to a classification. Furthermore, for many such problems, multiple established ranking models have been well studied and it is desirable to combine their results into a joint ranking, a formalism denoted as rank aggregation. This work presents a novel unsupervised learning algorithm for rank aggregation (ULARA) which returns a linear combination of the individual ranking functions based on the principle of rewarding ordering agreement between the rankers. In addition to presenting ULARA, we demonstrate its effectiveness on a data fusion task across ad hoc retrieval systems.

Original languageEnglish (US)
Title of host publicationMachine Learning
Subtitle of host publicationECML 2007 - 18th European Conference on Machine Learning, Proceedings
PublisherSpringer-Verlag Berlin Heidelberg
Pages616-623
Number of pages8
ISBN (Print)9783540749578
DOIs
StatePublished - Jan 1 2007
Event18th European Conference on Machine Learning, ECML 2007 - Warsaw, Poland
Duration: Sep 17 2007Sep 21 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4701 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other18th European Conference on Machine Learning, ECML 2007
CountryPoland
CityWarsaw
Period9/17/079/21/07

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Klementiev, A., Roth, D., & Small, K. (2007). An unsupervised learning algorithm for rank aggregation. In Machine Learning: ECML 2007 - 18th European Conference on Machine Learning, Proceedings (pp. 616-623). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4701 LNAI). Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-540-74958-5_60