Webpage recommendation with web navigation prediction framework Online publication date: Thu, 01-Sep-2016
by D. Sejal; T. Kamalakant; Dinesh Anvekar; K.R. Venugopal; S.S. Iyengar; L.M. Patnaik
International Journal of Knowledge and Web Intelligence (IJKWI), Vol. 5, No. 3, 2016
Abstract: 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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Knowledge and Web Intelligence (IJKWI):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com