Web Page Recommendations Based Web Navigation Prediction

Abstract

A huge amount of user request data is generated in Web log. Predicting users’ future requests based on previously visited pages is important for Web page recommendation, reduction of latency and on-line advertising. These applications compromise with prediction accuracy and modelling complexity. In this chapter, a Web Navigation Prediction Framework for Web page Recommendation (WNPWR) which creates and generates a classifier based on sessions as training examples is proposed. As sessions are used as training examples, they are created by calculating the average time on visiting Web pages rather than the traditional method which uses 30 min as default time-out. The proposed method uses standard benchmark datasets to analyse and compare our framework with two-tier prediction framework. Simulation results show that our generated classifier framework WNPWR outperforms two-tier