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dc.contributor.advisorJohansen, Dag
dc.contributor.authorMortensen, Magnus
dc.date.accessioned2007-03-16T07:35:30Z
dc.date.available2007-03-16T07:35:30Z
dc.date.issued2007-02-05
dc.description.abstractIn the recent years, the Web has undergone a tremendous growth regarding both content and users. This has lead to an information overload problem in which people are finding it increasingly difficult to locate the right information at the right time. Recommender systems have been developed to address this problem, by guiding users through the big ocean of information. Until now, recommender systems have been extensively used within e-commerce and communities where items like movies, music and articles are recommended. More recently, recommender systems have been deployed in online music players, recommending music that the users probably will like. This thesis will present the design, implementation, testing and evaluation of a recommender system within the music domain, where three different approaches for producing recommendations are utilized. Testing each approach is done by first conducting live user experiments and then measure recommender precision using offline analysis. Our results show that the functionality of the recommender system is satisfactory, and that recommender precision differs for the three filtering approaches.en
dc.format.extent750884 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10037/762
dc.identifier.urnURN:NBN:no-uit_munin_407
dc.language.isoengen
dc.publisherUniversitetet i Tromsøen
dc.publisherUniversity of Tromsøen
dc.rights.accessRightsopenAccess
dc.rights.holderCopyright 2007 The Author(s)
dc.subjectVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550::Datateknologi: 551en
dc.subjectmusicen
dc.subjectsystemen
dc.subjectdistribuerten
dc.subjectinternetten
dc.subjectpresisjonen
dc.subjectanbefalingen
dc.subjectfiltreringen
dc.subjecttilbakemeldingen
dc.subjecte-handelen
dc.subjecttilbakemeldingen
dc.subjectbrukereen
dc.subjectskalerbarheten
dc.subjectrecommenderen
dc.subjectfilteringen
dc.subjectcollaborativeen
dc.subjectcontent-baseden
dc.subjectcontexten
dc.subjectmooden
dc.subjectfeedbacken
dc.subjectprecisionen
dc.subjectinformation overloaden
dc.subjectmean squared differenceen
dc.subjectweben
dc.subjecte-commerceen
dc.subjectretrievalen
dc.subjectsparsityen
dc.subjectintrusivenessen
dc.subjectdistributeden
dc.subjectempiricalen
dc.subjectscalabilityen
dc.subjectmodelen
dc.subjectmemoryen
dc.subjectmp3en
dc.titleDesign and evaluation of a recommender systemen
dc.typeMaster thesisen
dc.typeMastergradsoppgaveen


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