Experimental Adaptable Recommendation System based on Web Usage Mining

H. Ishikawa, T. Nakajima, T. Mizuhara, S. Yokoyama, J. Nakayama, M. Ohta, and K. Katayama (Japan)


Web usage mining, recommendation system, user model, collaborative filtering


As an increasing number of Web sites consist of an increasing number of pages, it is more difficult for the users to rapidly reach their own target pages. So the systems supporting the users in navigation of the Web contents are in high demand. In this paper, we describe a recommendation system called the system L-R (Log-based Recommendation), which constructs user models by mining the Web access logs and recommends the relevant pages to the users based both on the user models and the Web contents.

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