M. Gopalachari, P. Sammulal
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引用次数: 19
Meta Data based Conceptualization and Temporal Semantics in Hybrid Recommender
Modernrecommendersystemstarget thesatisfactionof theenduser throughthepersonalization techniquesthatcollectsthehistoryoftheuser’snavigation.Butthesoledependencyontheuserprofile bymeansofnavigationhistoryalonecannotpromisethequalityofrecommendationsbecauseofthe lackofsemantics.Thoughtheliteratureprovidesmanytechniquestoconceptualizetheprocessthey leadtohighcomputationalcomplexityduetoconsideringthecontentdataasinputinformation.In thispaperahybridrecommenderframeworkisdevelopedthatconsidersMetadatabasedconceptual semantics and the temporal patterns on top of the usage history. This framework also includes anonlineprocessthat identifiestheconceptualdriftof theusagedynamically.Theexperimental resultsshowntheeffectivenessoftheproposedframeworkwhencomparedtotheexistingmodern recommendersalsoindicatethattheproposedmodelcanresolveacoldstartproblemyetaccurate suggestionsreducingcomputationalcomplexity. KeywoRDS Collaborative Filtering, Concept Drift, Domain Ontology, Recommendation System, Sequential Patterns, Temporal Semantics, Web Usage Mining
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