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