Theoretical categorization of query performance predictors

Victor Makarenkov, Bracha Shapira, Lior Rokach

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

2 Scopus citations

Abstract

The query-performance prediction task aims at estimating the retrieval effectiveness of queries without obtaining relevance feedback from users. Most of the recently proposed predictors were empirically evaluated with various datasets to demonstrate their merits. We propose a framework for theoretical categorization and estimation of the value of query performance predictors (QPP) without empirical evaluation. We demonstrate the application of the proposed framework on four representative selected predictors and show how it emphasizes their strengths and weaknesses. The main contribution of this work is the theoretical grounded categorization of representative QPP.

Original languageEnglish
Title of host publicationICTIR 2015 - Proceedings of the 2015 ACM SIGIR International Conference on the Theory of Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages369-372
Number of pages4
ISBN (Electronic)9781450338332
DOIs
StatePublished - 27 Sep 2015
Event5th ACM SIGIR International Conference on the Theory of Information Retrieval, ICTIR 2015 - Northampton, United States
Duration: 27 Sep 201530 Sep 2015

Publication series

NameICTIR 2015 - Proceedings of the 2015 ACM SIGIR International Conference on the Theory of Information Retrieval

Conference

Conference5th ACM SIGIR International Conference on the Theory of Information Retrieval, ICTIR 2015
Country/TerritoryUnited States
CityNorthampton
Period27/09/1530/09/15

Keywords

  • Categorization
  • Query-performance prediction
  • Theoretical evaluation

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