Query performance prediction: evaluation contrasted with effectiveness

Claudia Hauff, Leif Azzopardi, Djoerd Hiemstra, Franciska de Jong

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

11 Citations (Scopus)

Abstract

Query performance predictors are commonly evaluated by reporting correlation coefficients to denote how well the methods perform at predicting the retrieval performance of a set of queries. Despite the amount of research dedicated to this area, one aspect remains neglected: how strong does the correlation need to be in order to realize an improvement in retrieval effectiveness in an operational setting? We address this issue in the context of two settings: Selective Query Expansion and Meta-Search. In an empirical study, we control the quality of a predictor in order to examine how the strength of the correlation achieved, affects the effectiveness of an adaptive retrieval system. The results of this study show that many existing predictors fail to achieve a correlation strong enough to reliably improve the retrieval effectiveness in the Selective Query Expansion as well as the Meta-Search setting.
Original languageEnglish
Title of host publicationAdvances in Information Retrieval
Subtitle of host publication32nd European Conference on IR Research, ECIR 2010, Milton Keynes, UK, March 28-31, 2010, Proceedings
Place of PublicationBerlin, Heidelberg
PublisherSpringer-Verlag
Pages204-216
Number of pages13
ISBN (Print)3-642-12274-4, 978-3-642-12274-3
DOIs
Publication statusPublished - 2010
Externally publishedYes

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume5993

Keywords

  • data mining
  • knowledge discovery
  • information systems

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