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Reseach Article

Web Navigation Path Pattern Prediction using First Order Markov Model and Depth first Evaluation

by V.valli Mayil
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 45 - Number 16
Year of Publication: 2012
Authors: V.valli Mayil
10.5120/6865-9464

V.valli Mayil . Web Navigation Path Pattern Prediction using First Order Markov Model and Depth first Evaluation. International Journal of Computer Applications. 45, 16 ( May 2012), 26-31. DOI=10.5120/6865-9464

@article{ 10.5120/6865-9464,
author = { V.valli Mayil },
title = { Web Navigation Path Pattern Prediction using First Order Markov Model and Depth first Evaluation },
journal = { International Journal of Computer Applications },
issue_date = { May 2012 },
volume = { 45 },
number = { 16 },
month = { May },
year = { 2012 },
issn = { 0975-8887 },
pages = { 26-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume45/number16/6865-9464/ },
doi = { 10.5120/6865-9464 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:37:46.331617+05:30
%A V.valli Mayil
%T Web Navigation Path Pattern Prediction using First Order Markov Model and Depth first Evaluation
%J International Journal of Computer Applications
%@ 0975-8887
%V 45
%N 16
%P 26-31
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Web usage mining has been defined as a technique of finding hidden knowledge from a log file. The interaction between website and user is recorded in the related web server log file. Web designer is able to analyze the file in order to understand the interaction between users and a web site, which helps to improve web topology. All information of web usage can be generated from log files and it consists of set of navigation sessions that represent the trails formed by users during the navigation process. In this paper, user web navigation sessions are inferred from log data and are modeled as a Markov chain. The chain's higher probability trails will be the most likely preferred trails on the web site. The algorithm discussed in this paper implements a depth-first search that scans the Markov chain for the high probability trails. The approaches result in prediction of popular web path and user navigation behavior. Web link prediction is the process to predict the Web pages to be visited by a user based on the Web pages previously visited by other user.

References
  1. Bettina Berent, Bamshad Mobasher, "Myra Spiliopoulou, and Jim Wiltshire. Measuring the accuracy of sessionizers for web usage analysis. " In Proceedings of the Web Mining Workshop at the First SIAM International Conference on Data Mining, pages 7–14, Chicago, April 2001
  2. M. Eirinaki, M. Vazirgiannis , "Web Path Recommendations based on Page Ranking and Markov Models", Proceedings on 7th ACM International Workshop Web Information and Data Management (WIDM '05), pp. 2-9, 2005
  3. J. Borges and M. Levene, "An Average Linear Time Algorithm for Web Usage Mining", Int'l journal Information Technology and decision Making , Vol. 3, no. 2, pp. 307-319, June 2004.
  4. Jos´e Borges and Mark Levene. "Data mining of user navigation patterns. ", In Brij Masand and Myra Spliliopoulou, editors, Web Usage Analysis and User Profiling, Lecture Notes in Artificial Intelligence (LNAI 1836), pages 92–111. Springer Verlag, Berlin, 2000.
  5. Jose Borges and Mark Levene, "Evaluating Variable-Length Markov Chain Models for Analysis of User Web Navigation Sessions", IEEE Transactions on Knowledge and Data Engineering, Vol 19, No 4, April 2007.
  6. R. Sarukkai. "Link prediction and path analysis using markov chains", in Computer Networks, Vol. 33, No. 1, June 2000, pp. 377-386.
  7. M. Spiliopoulou and C. Pohle. "Data mining for measuring and improving the success of web sites". Data Mining and Knowledge Discovery, 5:85–114, 2001.
Index Terms

Computer Science
Information Sciences

Keywords

Web Navigation Markov Model Depth First Evaluation Transition And Trail Probability