Ontology based web usage mining model
Ch. Ramesh, K. Venkateswara Rao, Aliseri Govardhan · 2017
Due to unprecedented growth of information on the Web and lack of structure in many Web sites, it became real challenge to the Web users to find relevant information. To solve this problem, Personalization is considered as a popular solution to customize the World Wide Web environment toward the user's preferences. Recent study shows that Web Usage Mining techniques play an important role in designing Web page recommendation systems. However the present conventional content-based recommender systems, built using the Web Usage Mining process, do not take Semantic Knowledge into pattern discovery and recommendation process. Recent studies show that integrating domain knowledge in the form of ontology into Web Usage Mining process can enhance the quality of the discovered usage patterns. Our work aims to incorporate semantics knowledge in all the phases of Web Usage mining process. CloSpan, a state-of-the-art algorithm for Sequential Pattern mining is applied over the Semantic space to generate frequent Sequential Patterns. The generated semantically enriched patterns are fed to Web page Recommendation model in offline phase. Experimental results shown are promising and showed a significant improvement on the quality of the recommendations.