Estimating query representativeness for query-performance prediction

Mor Sondak, Anna Shtok, Oren Kurland

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

Abstract

The query-performance prediction (QPP) task is estimating retrieval effectiveness with no relevance judgments. We present a novel probabilistic framework for QPP that gives rise to an important aspect that was not addressed in previous work; namely, the extent to which the query effectively represents the information need for retrieval. Accordingly, we devise a few query-representativeness measures that utilize relevance language models. Experiments show that integrating the most effective measures with state-of-the-art predictors in our framework often yields prediction quality that significantly transcends that of using the predictors alone.

Original languageEnglish
Title of host publicationSIGIR 2013 - Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval
Pages853-856
Number of pages4
DOIs
StatePublished - 2013
Event36th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2013 - Dublin, Ireland
Duration: 28 Jul 20131 Aug 2013

Publication series

NameSIGIR 2013 - Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

Conference36th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2013
Country/TerritoryIreland
CityDublin
Period28/07/131/08/13

Keywords

  • Query-performance prediction

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Information Systems

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