On the Effectiveness of Web Usage Mining for Page Recommendation and Restructuring

Abstract

The more pages Web sites consist of, the more difficult the users find it to rapidly reach their own target pages. Ill-structured design of Web sites also prevents the users from rapidly accessing the target pages. In this paper, we describe two complementary approaches to Web usage mining as a key solution to these issues. First, we describe an adaptable recommendation system called the system L-R, which constructs user models by classifying the Web access logs and by extracting access patterns based on the transition probability of page accesses and recommends the relevant pages to the users based both on the user models and the Web contents. We have evaluated the prototype system and have obtained the positive effects. Second, we describe another approach to constructing user models, which clusters Web access logs based on access patterns. We also have found that the user models help to discover unexpected access paths corresponding to ill-formed Web site design.