Selective cluster-based document retrieval

Or Levi, Fiana Raiber, Oren Kurland, Ido Guy

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

10 Scopus citations


We address the long standing challenge of selective cluster-based retrieval; namely, deciding on a per-query basis whether to apply cluster-based document retrieval or standard document retrieval. To address this classification task, we propose a few sets of features based on those utilized by the cluster-based ranker, query-performance predictors, and properties of the clustering structure. Empirical evaluation shows that our method outperforms state-of-the-art retrieval approaches, including cluster-based, query expansion, and term proximity methods.

Original languageEnglish
Title of host publicationCIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Number of pages10
ISBN (Electronic)9781450340731
StatePublished - 24 Oct 2016
Event25th ACM International Conference on Information and Knowledge Management, CIKM 2016 - Indianapolis, United States
Duration: 24 Oct 201628 Oct 2016

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings


Conference25th ACM International Conference on Information and Knowledge Management, CIKM 2016
Country/TerritoryUnited States

ASJC Scopus subject areas

  • General Decision Sciences
  • General Business, Management and Accounting


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