Adaptive neural network clustering of Web users

The degree of personalization that a Web site offers in presenting its services to users is an important attribute contributing to the site's popularity. Web server access logs contain substantial data about user access patterns. One way to solve this problem is to group users on the basis of their Web interests and then organize the site's structure according to the needs of different groups. Two main difficulties inhibit this approach: the essentially infinite diversity of user interests and the change in these interests with time. We have developed a clustering algorithm that groups users according to their Web access patterns. The algorithm is based on the ART1 version of adaptive resonance theory. In our ART1-based algorithm, a prototype vector represents each user cluster by generalizing the URLs most frequently accessed by all cluster members. We have compared our algorithm's performance with the traditional k-means clustering algorithm. Results showed that the ART1-based technique performed better in terms of intracluster distances. We also applied the technique in a prefetching scheme that predicts future user requests.