[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.9708/jksci.2020.25.12.165

Framework for Efficient Web Page Prediction using Deep Learning

Kim, Kyung-Chang (Dept. of Computer Engineering, Hongik University)

Publication Information

Journal of the Korea Society of Computer and Information / v.25, no.12, 2020 , pp. 165-172 More about this Journal

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.

Keywords

Deep learning; Framework; Web page prediction; Web log; Log preprocessing; MapReduce model;

Citations & Related Records

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