An approach to intelligent Web pre-fetching based on hidden Markov model

With the exponential growth of information on the Web, Internet has become one of the most important information sources. However, due to limitation of the network bandwidth, users always have to bear with long time waiting. Web pre-fetching solution is one of the most popular strategies, which is proposed for reducing the perceived access delay and improving the service quality of Web server. This paper proposes a pre-fetching model based on the hidden Markov model (HMM), and utilizes HMM to capture and mine the latent information requirement concepts that the user´s access path contains and to make semantic-based pre-fetching decisions. Experimental results show that our scheme has better predictive pre-fetching precision and evidently reduces the users´ access time.