Integrating Markov Model with KNN Classification for Web Page Prediction

World Wide Web is growing rapidly in recent years. User’s experience on the internet can be improved by minimizing user’s web access latency. This can be done by predicting the next step taken by user towards the accessing of web page in advance, so that the predicted web page can be prefetched and cached. So to improve the quality of web services, it is required to analyze the user web navigation behavior. Analysis of user web navigation behavior is achieved through modeling web navigation history. Markov model is widely used to model the user web navigation sessions. Although traditional Markov models have helped predict user access behavior, they have serious limitations In this paper, we analyze and study Markov model and all-Kth Markov model in Web prediction. We propose new two-tier prediction frameworks that classify the user sessions, based on the KNN algorithm and then the Kth Markov Model is applied to predict the next web page. We show that such framework can improve the prediction time without compromising prediction accuracy and provides better performance over build time, search time, memory used and error rate.