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dc.contributor.authorAltingovde, I. S.en_US
dc.contributor.authorSubakan, Ö. N.en_US
dc.contributor.authorUlusoy, Ö.en_US
dc.date.accessioned2016-02-08T09:41:54Z
dc.date.available2016-02-08T09:41:54Z
dc.date.issued2013en_US
dc.identifier.issn0306-4573
dc.identifier.urihttp://hdl.handle.net/11693/21149
dc.description.abstractIn-memory nearest neighbor computation is a typical collaborative filtering approach for high recommendation accuracy. However, this approach is not scalable given the huge number of customers and items in typical commercial applications. Cluster-based collaborative filtering techniques can be a remedy for the efficiency problem, but they usually provide relatively lower accuracy figures, since they may become over-generalized and produce less-personalized recommendations. Our research explores an individualistic strategy which initially clusters the users and then exploits the members within clusters, but not just the cluster representatives, during the recommendation generation stage. We provide an efficient implementation of this strategy by adapting a specifically tailored cluster- skipping inverted index structure. Experimental results reveal that the individualistic strategy with the cluster-skipping index is a good compromise that yields high accuracy and reasonable scalability figures. © 2012 Elsevier Ltd. All rights reserved.en_US
dc.language.isoEnglishen_US
dc.source.titleInformation Processing & Managementen_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.ipm.2012.07.008en_US
dc.subjectClusteringen_US
dc.subjectCollaborative filteringen_US
dc.subjectInverted indexen_US
dc.subjectCollaborative filtering techniquesen_US
dc.subjectCollaborative recommendation systemen_US
dc.subjectCommercial applicationsen_US
dc.subjectEfficient implementationen_US
dc.subjectInverted index structuresen_US
dc.subjectInverted indicesen_US
dc.subjectRecommendation accuracyen_US
dc.subjectCollaborative filteringen_US
dc.subjectComputer supported cooperative worken_US
dc.titleCluster searching strategies for collaborative recommendation systemsen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage688en_US
dc.citation.epage697en_US
dc.citation.volumeNumber49en_US
dc.citation.issueNumber3en_US
dc.identifier.doi10.1016/j.ipm.2012.07.008en_US


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