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dc.contributor.authorGuti´errez-Soto, C
dc.contributor.authorPALOMINO, MARCO
dc.contributor.authorCuriel, A
dc.contributor.authorCerda, HER
dc.contributor.authorRain, FB
dc.date.accessioned2021-08-09T17:30:20Z
dc.date.available2021-08-09T17:30:20Z
dc.date.issued2020-12-08
dc.identifier.issn2415-6698
dc.identifier.issn2415-6698
dc.identifier.urihttp://hdl.handle.net/10026.1/17528
dc.description.abstract

It is well documented that the average length of the queries submitted to Web search engines is rather short, which negatively impacts the engines’ performance, as measured by the precision metric. It is also well known that ambiguous keywords in a query make it hard to identify what exactly search engine users are looking for. One way to tackle this challenge is to consider the context in which the query is submitted, making use of query-sensitive similarity measures (QSSM). In this paper, a particular QSSM known as the query-document similarity measure (QDSM) is evaluated, QDSM is designed to determine the similarity between two queries based on their terms and their ranked lists of relevant documents. To this extent, F-measure and the nearest neighbor (NN) have been employed to assess this approach over a collection of AOL query logs. Final results reveal that both the Average Link Algorithm and Ward’s method present better results using QDSM than cosine similarity.

dc.format.extent883-893
dc.language.isoen
dc.publisherASTES Journal
dc.titleEvaluating the Effectiveness of Query-Document Clustering Using the QDSM Measure
dc.typejournal-article
dc.typeJournal Article
plymouth.issue6
plymouth.volume5
plymouth.publication-statusPublished
plymouth.journalAdvances in Science, Technology and Engineering Systems Journal
dc.identifier.doi10.25046/aj0506105
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dcterms.dateAccepted2020-11-19
dc.rights.embargodate2021-8-12
dc.identifier.eissn2415-6698
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.25046/aj0506105
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2020-12-08
rioxxterms.typeJournal Article/Review


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