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A SEMANTICALLY ENRICHED WEB USAGE BASED RECOMMENDATION MODEL

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Author(s): C.Ramesh | K. V. Chalapati Rao | K. V. Chalapati Rao

Journal: International Journal of Computer Science & Information Technology
ISSN 0975-4660

Volume: 3;
Issue: 5;
Start page: 193;
Date: 2011;
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Keywords: Web Usage Mining | Semantic Web | Domain Ontology | Sequential Pattern Mining | Recommender Systems

ABSTRACT
With the rapid growth of internet technologies, Web has become a huge repository of information andkeeps growing exponentially under no editorial control. However the human capability to read, accessand understand Web content remains constant. This motivated researchers to provide Web personalizedonline services such as Web recommendations to alleviate the information overload problem and providetailored Web experiences to the Web users. Recent studies show that Web usage mining has emerged as apopular approach in providing Web personalization. However conventional Web usage basedrecommender systems are limited in their ability to use the domain knowledge of the Web application.The focus is only on Web usage data. As a consequence the quality of the discovered patterns is low. Inthis paper, we propose a novel framework integrating semantic information in the Web usage miningprocess. Sequential Pattern Mining technique is applied over the semantic space to discover the frequentsequential patterns. The frequent navigational patterns are extracted in the form of Ontology instancesinstead of Web page views and the resultant semantic patterns are used for generating Web pagerecommendations to the user. Experimental results shown are promising and proved that incorporatingsemantic information into Web usage mining process can provide us with more interesting patterns whichconsequently make the recommendation system more functional, smarter and comprehensive
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