An Empirical Analysis of Web Navigation Prediction Techniques

Honey Jindal (Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, India) and Neetu Sardana (Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, India)
Copyright: © 2017 |Pages: 14
EISBN13: 9781522533535|DOI: 10.4018/jcit.2017010101
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Abstract

With the advancement of Information Technology, web is growing rapidly and it has became necessary part of our daily lives. It is mandate to study the navigation behavior of the user to improve the quality of web site design for personalization and further recommendation. Analysis of web navigation behavior heavily relies on navigational models. This paper is an effort to give insights of current state-of-the-art techniques used for web navigation prediction. These navigation models are broadly classified into three categories: sequential mining, classification and clustering. Analytical analysis is performed on all the categories used in web navigation prediction. Further empirical analysis is performed on popular techniques of each category Markov Model (sequential mining), Support vector machine (classification) and K-means (clustering) on the common platform to measure the effectiveness of these techniques.
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