A framework for combining markov model with association rules for predicting web page accesses

The importance of predicting Web users ’ behaviour and their next movement has been recognised and dis-cussed by many researchers lately. Association rules and Markov models are the most commonly used ap-proaches for this type of prediction. Association rules tend to generate many rules, which result in con-tradictory predictions for a user session. Low order Markov models do not use enough user browsing his-tory and therefore, lack accuracy, whereas, high or-der Markov models incur high state space complexity. This paper proposes a novel approach that integrates both association rules and low order Markov mod-els in order to achieve higher accuracy with low state space complexity. A low order Markov model pro-vides high coverage with low state space complexity, and association rules help achieve better accuracy