Semantic-enhanced web-page recommender systems

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dc.contributor.author Nguyen, Thi Thanh Sang
dc.date.accessioned 2013-07-04T05:08:14Z
dc.date.available 2013-07-04T05:08:14Z
dc.date.issued 2012
dc.identifier.uri http://hdl.handle.net/10453/23387
dc.description University of Technology, Sydney. Faculty of Engineering and Information Technology. en_US
dc.description.abstract This thesis presents a new framework for a semantic-enhanced Web-page recommender (WPR) system, and a suite of enabling techniques which include semantic network models of domain knowledge and Web usage knowledge, querying techniques, and Web-page recommendation strategies. The framework enables the system to automatically discover and construct the domain and Web usage knowledge bases, and to generate effective Webpage recommendations. The main contributions of the framework are fourfold: (1) it effectively changes the fact that knowledge base construction must rely on human experts; (2) it enriches the pool of candidate Web-pages for effective Web-page recommendations by using semantic knowledge of both Web-pages and Web usage; (3) it thoroughly resolves the inconsistency problem facing contemporary WPR systems which heavily employ heterogeneous representations of knowledge bases. Knowledge bases in the system are consistently represented in a formal Web ontology language, namely OWL; and (4) it can generate effective Web-page recommendations based on a set of thoughtfully-designed recommendation strategies. A prototype of the semantic-enhanced WPR system is developed and presented, and the experimental comparisons with existing WPR approaches convincingly prove the significantly improved performance of WPR systems based on the framework and its enabling techniques. en_US
dc.language.iso en en_US
dc.subject Semantic web. en
dc.subject Ontologies. en
dc.subject Web-page recommender systems. en
dc.subject Web usage mining. en
dc.title Semantic-enhanced web-page recommender systems en_US
dc.type Thesis (PhD) en_US


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