Semantically Enriched Web Usage Mining for Predicting User Future Movements

Explosive and quick growth of the World Wide Web has resulted in intricate Web sites, demanding enhanced user skills and sophisticated tools to help the Web user to find the desired information. Finding desired information on the Web has become a critical ingredient of everyday personal, educational, and business life. Thus, there is a demand for more sophisticated tools to help the user to navigate a Web site and find the desired information. The users must be provided with information and services specific to their needs, rather than an undifferentiated mass of information. For discovering interesting and frequent navigation patterns from Web server logs many Web usage mining techniques have been applied. The recommendation accuracy of solely usage based techniques can be improved by integrating Web site content and site structure in the personalization process. Herein, we propose Semantically enriched Web Usage Mining method (SWUM), which combines the fields of Web Usage Mining and Semantic Web. In the proposed method, the undirected graph derived from usage data is enriched with rich semantic information extracted from the Web pages and the Web site structure. The experimental results show that the SWUM generates accurate recommendations with integration of usage, semantic data and Web site structure. The results shows that proposed method is able to achieve 10-20% better accuracy than the solely usage based model, and 5-8% better than an ontology based model.