Classification for Web Page Prediction

The uncontrolled nature of Web content presents additional challenges to Web page classification as compared to traditional text classification, but the interconnected nature of hyper text also provides features that can assist the process. Web pages are not visited by the users in their accesses, the limited bandwidth of network and services of server will not be used efficiently and may face the access delay problem. Therefore, it is critical that we have an effective prediction method during perfecting. The Markov models have been widely used to predict and analyze user‘s navigational behavior. 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. The discovered patterns can be used for better web page access prediction. Prediction model are better prediction of next web page the user want to visit. Using web page access prediction, the right advertisement will be placed in the website according to the users' browsing patterns. In Web page prediction, the next action corresponds to predicting the next page to be visited.