An Approach to Web Page Prediction Using Markov Model and Web Page Ranking

Markov Models have been widely used for predicting next Web-page from the users’ navigational behavior recorded in the Web-log. This usage-based technique can be combined with the structural properties of the Web-pages to achieve better prediction accuracy. This paper proposes one of the pre-fetching techniques relying both on Markov Model and Ranking which considers the structural properties of the Web. In this paper, prediction accuracy is realized as a linear function of transition probability of first order Markov Model and ranking of the Webpage. The chance of the predicted Web-page being the next Web-page would be higher if the prediction accuracy of the Web-page is higher.