Predicting User's Web Navigation Behavior Using Hybrid Approach

AbstractWorld Wide Web is growing rapidly with different kinds of websites making it complex along with increasing traffic on the web. However predicting what the user wants becomes very difficult .There are various prediction challenges which are faced, some of them includes long training time, more prediction time, low prediction accuracy, memory limitation etc. The System aims to increase the prediction accuracy particularly when there are many prediction models to consult. The System also aims to reduce the complexity of prediction and yield efficient result and make the prediction user friendly as well minimize Miss-Prediction. The Hybrid model developed combines Markov model as well as Hidden Markov Model which gives user the list of web pages of their interest. We have used various kinds of datasets to analyze, compare and show the effectiveness of Hybrid model using various parameters such as Accuracy, Precision and Miss-Prediction