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Framework for Efficient Web Page Prediction using Deep Learning

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2020, 25(12), pp.165-172
  • DOI : 10.9708/jksci.2020.25.12.165
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : December 4, 2020
  • Accepted : December 23, 2020
  • Published : December 31, 2020

KIM KYUNG CHANG 1

1홍익대학교

Accredited

ABSTRACT

Recently, due to exponential growth of access information on the web, the importance of predicting a user’s next web page use has been increasing. One of the methods that can be used for predicting user’s next web page is deep learning. To predict next web page, web logs are analyzed by data preprocessing and then a user’s next web page is predicted on the output of the analyzed web logs using a deep learning algorithm. In this paper, we propose a framework for web page prediction that includes methods for web log preprocessing followed by deep learning techniques for web prediction. To increase the speed of preprocessing of large web log, a Hadoop based MapReduce programming model is used. In addition, we present a web prediction system that uses an efficient deep learning technique on the output of web log preprocessing for training and prediction. Through experiment, we show the performance improvement of our proposed method over traditional methods. We also show the accuracy of our prediction.

Citation status

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