An Adaptive PPM Prediction Model Based on Pruning Technique

摘要

The key issue of Web prefetch ing is to establish an effective user prediction model, which can be used to make precision prediction of user browsing actions. Prediction by Partial Match (PPM) is one of the context mod-eJs used in the Web prefetching area. The high space complexity and low efficiency of the PPM prediction model affect its application. In this paper, we make use of pruning technique and propose a new adaptive PPM model based on Zipf's law and Web access characteristics. The experiments have shown that this model not only can be used to make predictions dynamically, but also has relative lower space complexity and higher prediction accuracy.