Using semantic Web to reduce the colt-sart

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dc.contributor.authorNouali, Omar
dc.contributor.authorBelloui, Amokrane
dc.date.accessioned2013-11-21T13:36:35Z
dc.date.available2013-11-21T13:36:35Z
dc.date.issued2009-06
dc.description.abstractCollaborative filtering systems suffer from the cold-start problems (evaluation matrix, new user/new resource problem...). In this paper, we show that using semantic information describing users and resources can reduce the problems and lead to a better precision, coverage and quality for the recommendation engine. Semantic web is the infrastructure used for managing such semantic descriptions. We also present here the results of a set of evaluation experiments.fr_FR
dc.identifier.isrnCERIST-DTISI/RR--09-000000011--dzfr_FR
dc.identifier.urihttp://dl.cerist.dz/handle/CERIST/283
dc.publisherCERIST
dc.relation.ispartofRapports de recherche internes
dc.relation.ispartofseriesRapports de recherche internes
dc.relation.placeAlger
dc.subjectCollaborative filteringfr_FR
dc.subjectSemantic webfr_FR
dc.subjectRecommendation systemsfr_FR
dc.subjectCold-startfr_FR
dc.titleUsing semantic Web to reduce the colt-sartfr_FR
dc.typeTechnical Report
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