A Markov-based Web Prediction Model

A web prediction model helps to predict user requests ahead of time, making web servers more responsive. Several prediction techniques have been tried in the past; Markov based prediction models being the most popular ones. Among these, the All-K-order Markov model has been found to be most effective. We designed a Markov tree, which is a fourth order model but behaves like an All-K-order Markov model because of its ability to recognize different order models according to the height of the tree. It has dual characteristics of good applicability and predictive accuracy. Further, the model can be pruned to eliminate states that have very little contribution towards the accuracy of the model. In this work, an evolutionary model is designed that makes use of a fitness function. The fitness function is a weighted sum of precision and the coverage of the model. It determines if further pruning is required. The resultant model has reduced complexity. The evolutionary approach helps to train the model to make predictions commensurate to current web browsing patterns. Our model instances perform on par or better than All-K-order Markov model variants. However, we also observe good model accuracy consistently, when tested over dataset over a large time