Dynamic prediction of Web requests

As an increasing number of users access information on the World Wide Web, there is a opportunity to improve well known strategies for Web prefetching, dynamic user modeling and dynamic site customization in order to obtain better subjective performance and satisfaction in Web surfing. We propose a new method to exploit user navigational path behavior to predict, in real-time, future requests. Real-time user adaptation avoids the use of statistical techniques on Web logs by adopting a predictive user model. We designed a new model derived from the finite state machine (FSM) formalism together with an evolutionary algorithm that evolves a population of FSMs for achieving a good prediction rate, and we evaluated the performance of the prediction system using the concepts of precision and applicability.