Performance Based Novel Techniques for Semantic Web Mining

The explosive growth in the size and use of the World Wide Web continuously creates new great challenges and needs. The need for predicting the users' preferences in order to expedite and improve the browsing though a site can be achieved through personalizing of the websites. Most of the research efforts in web personalization correspond to the evolution of extensive research in web usage mining, i.e. the exploitation of the navigational patterns of the web site’s visitors. When a personalization system relies solely on usage-based results, however, valuable information conceptually related to what is finally recommended may be missed. Moreover, the structural properties of the web site are often disregarded. In this paper, we propose novel techniques that use the content semantics and the structural properties of a web site in order to improve the effectiveness of web personalization. In the first part of our work we present standing for Semantic Web Personalization, a personalization system that integrates usage data with content semantics, expressed in ontology terms, in order to compute semantically enhanced navigational patterns and effectively generate useful recommendations. To the best of our knowledge, our proposed technique is the only semantic web personalization system that may be used by non-semantic web sites. In the second part of our work, we present a novel approach for enhancing the quality of recommendations based on the underlying structure of a web site. We introduce UPR (Usage-based PageRank), a PageRank-style algorithm that relies on the recorded usage data and link analysis techniques. Overall, we demonstrate that our proposed hybrid personalization framework results in more objective and representative predictions than existing techniques. KeywordsWeb personalization, Semantic web and Recommender systems.