Paper Info
Title
WNPWR: Web navigation prediction framework for webpage recommendation
Abstract
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, on-line advertising etc. 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 average time on visiting web pages rather than traditional method which uses 30 minutes as default timeout. This paper uses standard benchmark datasets to analyze and compare our framework with two-tier prediction framework. Simulation results shows that our generated classifier framework WNPWR outperforms two-tier prediction framework in prediction accuracy and time.
Year
DOI
Venue
2015
10.1109/ReTIS.2015.7232864
2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)
Keywords
Field
DocType
Web navigation,Web prediction,Webpage recommendation
Data mining,Web page,Web mapping,Computer science,Support vector machine,Data Web,Timeout,Web modeling,Web navigation,Classifier (linguistics)
Conference
Citations 
PageRank 
References 
0
0.34
13
Authors
7
Name
Order
Citations
PageRank
D. Sejal141.83
T. Kamalakant200.68
V. Tejaswi322.76
Dinesh Anvekar401.35
K. R. Venugopal526748.80
S.S. Iyengar62923381.93
Lalit M. Patnaik724348.76