Using Hybrid Markov Model for Web Access Prediction

Abstract

Mining user patterns of log file can provide significant and useful informative knowledge. A large amount of research has been done on trying to predict correctly the pages a user will request. This task requires the development of models that can predicts a user’s next request to a web server. In this paper, we propose a method for constructing first-order and second-order Markov models of Web site access prediction based on past visitor behavior and compare it association rules technique. This algorithm has been used to cluster similar transition behaviors for efficient used to further improve the efficiency of prediction. From this comparison we propose a
best overall method and empirically test the proposed model on real web logs.