Publicación:
A CONTEXTUAL MODELING APPROACH FOR MODEL-BASED RECOMMENDER SYSTEMS

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dc.creatorPEDRO GERÓNIMO CAMPOS SOTO
dc.date2013
dc.date.accessioned2025-01-10T14:29:23Z
dc.date.available2025-01-10T14:29:23Z
dc.date.issued2013
dc.description.abstractIN THIS PAPER WE PRESENT A CONTEXTUAL MODELING APPROACH FOR MODEL-BASED RECOMMENDER SYSTEMS THAT INTEGRATES AND EXPLOITS BOTH USER PREFERENCES AND CONTEXTUAL SIGNALS IN A COMMON VECTOR SPACE. DIFFERENTLY TO PREVIOUS WORK, WE CONDUCT A USER STUDY ACQUIRING AND ANALYZING A VARIETY OF REALISTIC CONTEXTUAL SIGNALS ASSOCIATED TO USER PREFERENCES IN SEVERAL DOMAINS. MOREOVER, WE REPORT EMPIRICAL RESULTS EVALUATING OUR APPROACH IN THE MOVIE AND MUSIC DOMAINS, WHICH SHOW THAT ENHANCING MODEL-BASED RECOMMENDER SYSTEMS WITH TIME, LOCATION AND SOCIAL COMPANION INFORMATION IMPROVES THE ACCURACY OF GENERATED RECOMMENDATIONS.
dc.formatapplication/pdf
dc.identifier.doi10.1007/978-3-642-40643-0_5
dc.identifier.urihttps://repositorio.ubiobio.cl/handle/123456789/7656
dc.languagespa
dc.publisherCONFERENCE OF THE SPANISH ASSOCIATION FOR ARTIFICIAL INTELLIGENCE
dc.relation.uri10.1007/978-3-642-40643-0_5
dc.rightsPUBLICADA
dc.subjectRECOMMENDER SYSTEMS
dc.subjectMODEL-BASED
dc.subjectCONTEXTUAL MODELING
dc.subjectCONTEXT-AWARE RECOMMENDATION
dc.titleA CONTEXTUAL MODELING APPROACH FOR MODEL-BASED RECOMMENDER SYSTEMS
dc.typeACTA DE CONFERENCIA
dspace.entity.typePublication
ubb.EstadoPUBLICADA
ubb.Otra ReparticionDEPARTAMENTO DE SISTEMAS DE INFORMACION
ubb.SedeCONCEPCIÓN
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