Dynamic and memory efficient web page prediction model using LZ78 and LZW algorithms

Web access prediction has attracted significant attention in recent years. Web prefetching and some personalization systems use prediction algorithms. Most current applications that predict the next user Web page have an offline component that does the data preparation task and an online section that provides personalized content to the users based on their current navigational activities. In this paper we present an online prediction model that does not have an offline component and fit in the memory with good prediction accuracy. Our algorithm is based on LZ78 and LZW algorithms that are adapted for modeling the user navigation in Web. Our model decreases computational complexities which is a serious problem in developing online prediction systems. A performance evaluation is presented using real Web logs. This evaluation shows that our model needs much less memory than PPM family of algorithms with good prediction accuracy.