A survey of pre-retrieval query performance predictors

C. Hauff; Djoerd Hiemstra; Franciska M.G. de Jong

doi:10.1145/1458082.1458311

A survey of pre-retrieval query performance predictors

C. Hauff, Djoerd Hiemstra, Franciska M.G. de Jong

Databases (Former)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

126 Citations (Scopus)

26 Downloads (Pure)

Abstract

The focus of research on query performance prediction is to predict the effectiveness of a query given a search system and a collection of documents. If the performance of queries can be estimated in advance of, or during the retrieval stage, specific measures can be taken to improve the overall performance of the system. In particular, pre-retrieval predictors predict the query performance before the retrieval step and are thus independent of the ranked list of results; such predictors base their predictions solely on query terms, the collection statistics and possibly external sources such as WordNet. In this poster, 22 pre-retrieval predictors are categorized and assessed on three different TREC test collections.

Original language	Undefined
Title of host publication	CIKM '08: Proceeding of the 17th ACM conference on Information and knowledge mining
Place of Publication	Napa Valley, California, USA
Publisher	Association for Computing Machinery
Pages	1419-1420
Number of pages	2
ISBN (Print)	978-1-59593-991-3
DOIs	https://doi.org/10.1145/1458082.1458311
Publication status	Published - 2008
Event	17th ACM Conference on Information and Knowledge Management, CIKM 2008 - Napa Valley, United States Duration: 26 Oct 2008 → 30 Oct 2008 Conference number: 17

Publication series

Name
Publisher	ACM
Number	08332

Conference

Conference	17th ACM Conference on Information and Knowledge Management, CIKM 2008
Abbreviated title	CIKM
Country/Territory	United States
City	Napa Valley
Period	26/10/08 → 30/10/08

Keywords

IR-62542
CR-H.3.3
METIS-252113
EWI-14113

Access to Document

10.1145/1458082.1458311

Cite this

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title = "A survey of pre-retrieval query performance predictors",

abstract = "The focus of research on query performance prediction is to predict the effectiveness of a query given a search system and a collection of documents. If the performance of queries can be estimated in advance of, or during the retrieval stage, specific measures can be taken to improve the overall performance of the system. In particular, pre-retrieval predictors predict the query performance before the retrieval step and are thus independent of the ranked list of results; such predictors base their predictions solely on query terms, the collection statistics and possibly external sources such as WordNet. In this poster, 22 pre-retrieval predictors are categorized and assessed on three different TREC test collections.",

keywords = "IR-62542, CR-H.3.3, METIS-252113, EWI-14113",

author = "C. Hauff and Djoerd Hiemstra and {de Jong}, {Franciska M.G.}",

note = "10.1145/1458082.1458311 ; 17th ACM Conference on Information and Knowledge Management, CIKM 2008 ; Conference date: 26-10-2008 Through 30-10-2008",

year = "2008",

doi = "10.1145/1458082.1458311",

language = "Undefined",

isbn = "978-1-59593-991-3",

publisher = "Association for Computing Machinery",

number = "08332",

pages = "1419--1420",

booktitle = "CIKM '08: Proceeding of the 17th ACM conference on Information and knowledge mining",

address = "United States",

}

Hauff, C, Hiemstra, D & de Jong, FMG 2008, A survey of pre-retrieval query performance predictors. in CIKM '08: Proceeding of the 17th ACM conference on Information and knowledge mining., 10.1145/1458082.1458311, Association for Computing Machinery, Napa Valley, California, USA, pp. 1419-1420, 17th ACM Conference on Information and Knowledge Management, CIKM 2008, Napa Valley, California, United States, 26/10/08. https://doi.org/10.1145/1458082.1458311

A survey of pre-retrieval query performance predictors. / Hauff, C.; Hiemstra, Djoerd; de Jong, Franciska M.G.
CIKM '08: Proceeding of the 17th ACM conference on Information and knowledge mining. Napa Valley, California, USA: Association for Computing Machinery, 2008. p. 1419-1420 10.1145/1458082.1458311.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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KW - EWI-14113

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