A unified probabilistic framework for clustering correlated heterogeneous web objects
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
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This paper describes an approach for automatically classifying visitors of a web site according to their access patterns. User access logs are examined to discover clusters of users that exhibit similar information needs; e.g., users that access similar pages. This may result in a better understanding of how users visit the site, and lead to an improved organization of the hypertext documents for navigational convenience. More interestingly, based on what categories an individual user falls into, we can dynamically suggest links for him to navigate. In this paper, we describe the overall design of a system that implements these ideas, and elaborate on the preprocessing, clustering, and dynamic link suggestion tasks. We present some experimental results generated by analyzing the access log of a web site.