Using Relevance Feedback in Expert Search
Identifieur interne : 004815 ( Istex/Corpus ); précédent : 004814; suivant : 004816Using Relevance Feedback in Expert Search
Auteurs : Craig Macdonald ; Iadh OunisSource :
- [ 0302-9743 ]
Abstract
Abstract: In Enterprise settings, expert search is considered an important task. In this search task, the user has a need for expertise - for instance, they require assistance from someone about a topic of interest. An expert search system assists users with their “expertise need” by suggesting people with relevant expertise to the topic of interest. In this work, we apply an expert search approach that does not explicitly rank candidates in response to a query, but instead implicitly ranks candidates by taking into account a ranking of document with respect to the query topic. Pseudo-relevance feedback, aka query expansion, has been shown to improve retrieval performance in adhoc search tasks. In this work, we investigate to which extent query expansion can be applied in an expert search task to improve the accuracy of the generated ranking of candidates. We define two approaches for query expansion, one based on the initial of ranking of documents for the query topic. The second approach is based on the final ranking of candidates. The aims of this paper are two-fold. Firstly, to determine if query expansion can be successfully applied in the expert search task, and secondly, to ascertain if either of the two forms of query expansion can provide robust, improved retrieval performance. We perform a thorough evaluation contrasting the two query expansion approaches in the context of the TREC 2005 and 2006 Enterprise tracks.
Url:
DOI: 10.1007/978-3-540-71496-5_39
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In this search task, the user has a need for expertise - for instance, they require assistance from someone about a topic of interest. An expert search system assists users with their “expertise need” by suggesting people with relevant expertise to the topic of interest. In this work, we apply an expert search approach that does not explicitly rank candidates in response to a query, but instead implicitly ranks candidates by taking into account a ranking of document with respect to the query topic. Pseudo-relevance feedback, aka query expansion, has been shown to improve retrieval performance in adhoc search tasks. In this work, we investigate to which extent query expansion can be applied in an expert search task to improve the accuracy of the generated ranking of candidates. We define two approaches for query expansion, one based on the initial of ranking of documents for the query topic. The second approach is based on the final ranking of candidates. The aims of this paper are two-fold. Firstly, to determine if query expansion can be successfully applied in the expert search task, and secondly, to ascertain if either of the two forms of query expansion can provide robust, improved retrieval performance. We perform a thorough evaluation contrasting the two query expansion approaches in the context of the TREC 2005 and 2006 Enterprise tracks.</div></front></TEI><istex errorEnrichmentsRefBib="curl or Istex failure"><corpusName>springer</corpusName><author><json:item><name>Craig Macdonald</name><affiliations><json:string>Department of Computing Science, University of Glasgow, G12 8QQ, UK</json:string><json:string>E-mail: craigm@dcs.gla.ac.uk</json:string></affiliations></json:item><json:item><name>Iadh Ounis</name><affiliations><json:string>Department of Computing Science, University of Glasgow, G12 8QQ, UK</json:string><json:string>E-mail: ounis@dcs.gla.ac.uk</json:string></affiliations></json:item></author><language><json:string>eng</json:string></language><abstract>Abstract: In Enterprise settings, expert search is considered an important task. In this search task, the user has a need for expertise - for instance, they require assistance from someone about a topic of interest. 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Firstly, to determine if query expansion can be successfully applied in the expert search task, and secondly, to ascertain if either of the two forms of query expansion can provide robust, improved retrieval performance. 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In this search task, the user has a need for expertise - for instance, they require assistance from someone about a topic of interest. An expert search system assists users with their “expertise need” by suggesting people with relevant expertise to the topic of interest. In this work, we apply an expert search approach that does not explicitly rank candidates in response to a query, but instead implicitly ranks candidates by taking into account a ranking of document with respect to the query topic. Pseudo-relevance feedback, aka query expansion, has been shown to improve retrieval performance in adhoc search tasks. In this work, we investigate to which extent query expansion can be applied in an expert search task to improve the accuracy of the generated ranking of candidates. We define two approaches for query expansion, one based on the initial of ranking of documents for the query topic. The second approach is based on the final ranking of candidates. The aims of this paper are two-fold. Firstly, to determine if query expansion can be successfully applied in the expert search task, and secondly, to ascertain if either of the two forms of query expansion can provide robust, improved retrieval performance. We perform a thorough evaluation contrasting the two query expansion approaches in the context of the TREC 2005 and 2006 Enterprise tracks.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Using Relevance Feedback in Expert Search</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Using Relevance Feedback in Expert Search</title></titleInfo><name type="personal"><namePart type="given">Craig</namePart><namePart type="family">Macdonald</namePart><affiliation>Department of Computing Science, University of Glasgow, G12 8QQ, UK</affiliation><affiliation>E-mail: craigm@dcs.gla.ac.uk</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Iadh</namePart><namePart type="family">Ounis</namePart><affiliation>Department of Computing Science, University of Glasgow, G12 8QQ, UK</affiliation><affiliation>E-mail: ounis@dcs.gla.ac.uk</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference [eBooks]" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2007</dateIssued><copyrightDate encoding="w3cdtf">2007</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Abstract: In Enterprise settings, expert search is considered an important task. In this search task, the user has a need for expertise - for instance, they require assistance from someone about a topic of interest. An expert search system assists users with their “expertise need” by suggesting people with relevant expertise to the topic of interest. In this work, we apply an expert search approach that does not explicitly rank candidates in response to a query, but instead implicitly ranks candidates by taking into account a ranking of document with respect to the query topic. Pseudo-relevance feedback, aka query expansion, has been shown to improve retrieval performance in adhoc search tasks. In this work, we investigate to which extent query expansion can be applied in an expert search task to improve the accuracy of the generated ranking of candidates. We define two approaches for query expansion, one based on the initial of ranking of documents for the query topic. The second approach is based on the final ranking of candidates. The aims of this paper are two-fold. Firstly, to determine if query expansion can be successfully applied in the expert search task, and secondly, to ascertain if either of the two forms of query expansion can provide robust, improved retrieval performance. We perform a thorough evaluation contrasting the two query expansion approaches in the context of the TREC 2005 and 2006 Enterprise tracks.</abstract><relatedItem type="host"><titleInfo><title>Advances in Information Retrieval</title><subTitle>29th European Conference on IR Research, ECIR 2007, Rome, Italy, April 2-5, 2007. 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