Web usage mining approaches to page recommendation and restructuring

As an increasing number of Web sites such as e-businesses consist of an increasing number of pages, users find it more difficult 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 L-R system, 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 on both the user models and the Web structures. 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. The user models also help to discover unexpected access paths corresponding to ill-formed Web site design. Copyright © 2003 John Wiley & Sons, Ltd.