Title: Webpage recommendation with web navigation prediction framework

Authors: D. Sejal; T. Kamalakant; Dinesh Anvekar; K.R. Venugopal; S.S. Iyengar; L.M. Patnaik

Addresses: Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bangalore-560001, India ' Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bangalore-560001, India ' Alpha College of Engineering, Hennur – Bagalur Road, Kannur Post, Bengaluru, Karnataka 560077, India ' Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bangalore-560001, India ' ECS – 351, Florida International University, MMC Campus, 11200 S.W. 8th Street, Miami, FL 33199, USA ' National Institute of Advanced Studies, Indian Institute of Science Campus, Bangalore, Karnataka 560012, India

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.

Keywords: web navigation; web prediction; webpage recommendations; recommender systems; recommendation systems; webpage visits; webpage visiting times; simulation; classification.

DOI: 10.1504/IJKWI.2016.078733

International Journal of Knowledge and Web Intelligence, 2016 Vol.5 No.3, pp.230 - 251

Received: 25 Sep 2015
Accepted: 10 Mar 2016

Published online: 26 Aug 2016 *

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