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 t...

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Bibliographic Details
Published in:Computer (Long Beach, Calif.) Calif.), 2004-04, Vol.37 (4), p.34-40
Main Authors: Rangarajan, S.K., Phoha, V.V., Balagani, K.S., Selmic, R.R., Iyengar, S.S.
Format: Article
Language:English
Subjects:
Adaptive systems
Algorithms
Cluster analysis
Clustering algorithms
Feature extraction
Information processing
NASA
Neural networks
Prefetching
Prototypes
Resonance
Servers
Uniform resource locators
Web server
Web sites
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Summary: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.
ISSN:0018-9162
1558-0814
DOI:10.1109/MC.2004.1297299