WEB USER PREDICTION BY: INTEGRATING MARKOV MODEL WITH DIFFERENT FEATURES

Web page prefetching has been widely used to reduce the access latency problem of the Internet. However, if most prefetched web pages are not visited by the users in their subsequent accesses, the limited network bandwidth and server resources will not be used efficiently and may worsen the access delay problem. Therefore, it is critical that we have an accurate prediction method during prefetching. The technique like Markov models have been widely used to represent and analyze user's navigational behavior (usage data) in the Web graph, using the transitional probabilities between web pages, as recorded in the web logs. The recorded users ' navigation is used to extract popular web paths and predict current users ' next steps. In this paper, we analyze and study Markov model and all-Kth Markov model with webpage keywords as a feature to give more accurate results in Web prediction. Our experiments show the effectiveness of our modified Markov model in reducing the number of paths by propoer clustering without compromising accuracy