From Cluster Ranking to Document Ranking

Egor Markovskiy, Fiana Raiber, Shoham Sabach, Oren Kurland

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

Abstract

The common approach of using clusters of similar documents for ad hoc document retrieval is to rank the clusters in response to the query; then, the cluster ranking is transformed to document ranking. We present a novel supervised approach to transform cluster ranking to document ranking. The approach allows to simultaneously utilize different clusterings and the resultant cluster rankings; this helps to improve the modeling of the document similarity space. Empirical evaluation shows that using our approach results in performance that substantially transcends the state-of-the-art in cluster-based document retrieval.

Original languageEnglish
Title of host publicationSIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
Pages2137-2141
Number of pages5
ISBN (Electronic)9781450387323
DOIs
StatePublished - 6 Jul 2022
Event45th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2022 - Madrid, Spain
Duration: 11 Jul 202215 Jul 2022

Publication series

NameSIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

Conference45th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2022
Country/TerritorySpain
CityMadrid
Period11/07/2215/07/22

Keywords

  • ad hoc retrieval
  • cluster ranking
  • document ranking

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Information Systems
  • Software

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