Webpage recommendation with web navigation prediction framework. (2016) - Sejal, D.; Kamalakant, T.; Anvekar, Dinesh; Venugopal, K.R.; Iyengar, S.S.; Patnaik, L.M.
Huge amount of user request data is generated in web-log. Predicting users' future requests based on previously visited pages is important for webpage recommendation, reduction of latency and online advertising. These applications compromise with prediction accuracy and modelling complexity. We propose a web navigation prediction framework for webpage recommendation (WNPWR) which creates and generates a classifier based on sessions as training examples. As sessions are used as training examples, they are created by calculating the average time on visiting webpages rather than traditional method which uses 30 minutes as default timeout. This paper 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 prediction framework in prediction accuracy and time.