Incremental click-stream tree model: Learning from new users for web page prediction

Predicting the next request of a user has gained importance as Web-based activity increases in order to guide Web users during their visits to Web sites. Previously proposed methods for recommendation use data collected over time in order to extract usage patterns. However, these patterns may change over time, because each day new log entries are added to the database and old entries are deleted. Thus, over time it is highly desirable to perform the update of the recommendation model incrementally. In this paper, we propose a new model for modeling and predicting Web user sessions which attempt to reduce the online recommendation time while retaining predictive accuracy. Since it is very easy to modify the model, it is updated during the recommendation process. The incremental algorithm yields a better prediction accuracy as well as a shorter online recommendation time. A performance evaluation of Incremental Click-Stream Tree model over two different Web server access logs indicate that the proposed incremental model yields significant speed-up of recommendation time and improvement of the prediction accuracy.

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