We present a study of the automatic classification of web user navigation patterns and propose a novel approach to classifying user navigation patterns and predicting users' future requests. The approach is based on the combined mining of Web server logs and the contents of the retrieved web pages. The textual content of web pages is captured through extraction of character N-grams, which are combined with Web server log files to derive user navigation profiles. The approach is implemented as an experimental system, and its performance is evaluated based on two tasks: classification and prediction. The system achieves the classification accuracy of nearly 70% and the prediction accuracy of about 65%, which is about 20% higher than the classification accuracy by mining Web server logs alone. This approach may be used to facilitate better web personalization and website organization.
['Haibin Liu', 'Vlado Keselj']
Combined mining of Web server logs and web contents for classifying user navigation patterns and predicting users' future requests