English

Abstract

Web prediction is a classification problem in which we try to predict the preceding set of Web pages in which a user may visit supported on the knowledge of the previously visited pages. While serving the Internet user's behavior prediction can be applied effectively in various critical applications. Such application has usual tradeoffs between modeling complexity and prediction accuracy. In this paper we proposed artificial neural network (ANN) for predicting web by the user. In addition modified Markov model has been analysed and presented in prediction of web. A prediction framework uses ANN based on the training samples. By doing this the proposed framework shows the improved prediction time without compromising prediction accuracy. Web prediction is a classification scheme in which the next set of Web pages is predicted that user may visit pages depends on the knowledge of the previously visited pages. This information of user's history of navigation within a period of time is said to be session. These sessions provides source of data for training which are extracted from the logs of the Web servers, and they hold series of pages that users have visited with the visit date and duration. The Web prediction problem (WPP) can be simplified and utilized in major industrial applications such as wireless applications, caching systems and search engines. Consequently, it is critical to look for practical solutions in which it improves both prediction and training processes. The prediction process can be improved to reduce the user's access times during browsing, and it can simplify the network traffic by neglecting visiting needless pages. Once a prediction model for a definite Web site is available, the search engine can utilize it to cache the next set of pages that the users might visit. Those caching eases the latency problem of out looking Web documents certainly at the time of Internet traffic congestion periods. An additional extensive application of Web prediction is personalization, wherein users are sorted based on their desires and interests. The interests and desires of users are confined along with the previously visited sorted Web pages. Moreover, in the wireless domain, Web prediction in mobile is utilized to minimize the number of clicks required in wireless devices such as PDAs and smart phones that assists to alleviate problems related to the display size restrictions. The challenges in web prediction of both preprocessing and prediction are an issue now-a-days. Preprocessing challenges comprise handling huge amount of data that cannot fit in the computer memory, selecting optimum sliding window size, recognizing sessions, and extracting domain information. Prediction challenges comprise long prediction/ training time, memory limitation and low prediction accuracy.