Meta Data based Conceptualization and Temporal Semantics in Hybrid Recommender

Int. J. Rough Sets Data Anal. Pub Date : 2017-10-01 DOI:10.4018/IJRSDA.2017100104

M. Gopalachari, P. Sammulal

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引用次数: 19

Abstract

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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混合推荐中基于元数据的概念化和时间语义

Modernrecommendersystemstarget thesatisfactionof theenduser throughthepersonalization techniquesthatcollectsthehistoryoftheuser 'snavigation。Butthesoledependencyontheuserprofile bymeansofnavigationhistoryalonecannotpromisethequalityofrecommendationsbecauseofthe lackofsemantics。Thoughtheliteratureprovidesmanytechniquestoconceptualizetheprocessthey leadtohighcomputationalcomplexityduetoconsideringthecontentdataasinputinformation。In thispaperahybridrecommenderframeworkisdevelopedthatconsidersMetadatabasedconceptual语义和时间模式在使用历史的顶部。这个框架还包括anonlineprocessthat identifiestheconceptualdriftof theusagedynamically。Theexperimental resultsshowntheeffectivenessoftheproposedframeworkwhencomparedtotheexistingmodern recommendersalsoindicatethattheproposedmodelcanresolveacoldstartproblemyetaccurate suggestionsreducingcomputationalcomplexity。关键词协同过滤，概念漂移，领域本体，推荐系统，顺序模式，时间语义，Web使用挖掘

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Int. J. Rough Sets Data Anal.

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