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dc.contributor.advisorUlusoy, Özgüren_US
dc.contributor.authorSubakan, Özlem Nurcanen_US
dc.date.accessioned2016-07-01T11:03:19Z
dc.date.available2016-07-01T11:03:19Z
dc.date.issued2005
dc.identifier.urihttp://hdl.handle.net/11693/29692
dc.descriptionCataloged from PDF version of article.en_US
dc.description.abstractCollectively, a population contains vast amounts of knowledge and modern communication technologies that increase the ease of communication. However, it is not feasible for a single person to aggregate the knowledge of thousands or millions of data and extract useful information from it. Collaborative information systems are attempts to harness the knowledge of a population and to present it in a simple, fast and fair manner. Collaborative filtering has been successfully used in domains where the information content is not easily parse-able and traditional information filtering techniques are difficult to apply. Collaborative filtering works over a database of ratings for the items which are rated by users. The computational complexity of these methods grows linearly with the number of customers which can reach to several millions in typical commercial applications. To address the scalability concern, we have developed an efficient collaborative filtering technique by applying user clustering and using a specific inverted index structure (so called cluster-skipping inverted index structure) that is tailored for clustered environments. We show that the predictive accuracy of the system is comparable with the collaborative filtering algorithms without clustering, whereas the efficiency is far more improved.en_US
dc.description.statementofresponsibilitySubakan, Özlem Nurcanen_US
dc.format.extentxi, 67 leaves, illustrationsen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCollaborative filteringen_US
dc.subjectRecommender systemsen_US
dc.subjectClusteringen_US
dc.subjectInverted filesen_US
dc.subjectPerformance evaluationen_US
dc.subject.lccQA76.9.H85 S82 2005en_US
dc.subject.lcshHuman-computer interaction.en_US
dc.titleCluster based collaborative filtering with inverted indexingen_US
dc.typeThesisen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidBILKUTUPB093571


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