S.Kaviarasan, K.Hemapriya, K.Gopinath
The explosive growth of the World Wide Web (WWW) has resulted in intricate Web sites, demanding for tools and methods to complement user skills in the task of searching for the desired information. In this context Web usage mining techniques have been developed for the discovery and analysis of frequent navigation patterns from Web server logs, which can be used as input for recommendation engines. Web usage mining techniques have been associated with Web content mining approaches in order to increase the accuracy of recommendation mechanisms. Existing approaches represent Web pages’ content essentially by means of keywords, N-grams or ontologies of concepts, being, therefore, incapable of capturing the semantic information and the relationships among pages at the semantic level. Herein, we propose a method that combines usage patterns extracted from server logs with detailed semantic data that characterizes the content of the corresponding pages. Thus, a method to extract and analyze frequent semantic navigation patterns which are fed into a recommendation engine is proposed. We argue that by integrating usage and Web pages’ detailed semantic information in the personalization process we will be able to increase the recommendation accuracy. The proposed method is an example of semantic Web mining that combines two fast developing research areas; Semantic Web and Web Usage Mining. We conducted an extensive experimental evaluation that provides strong evidence that the recommendation accuracy increases with the integration of semantic and usage data. The results show that the proposed method is able to achieve 15-17% better accuracy than a usage based model, 5-7% better than a N-gram based model and 4-6% better than a ontology based model. Also the proposed method is able to address the new item problem of solely usage based techniques by augmenting navigation patterns with newly added pages in a Web site
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