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Web access prediction based on parallel deep learning

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2019, 24(11), pp.51-59
  • DOI : 10.9708/jksci.2019.24.11.051
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : October 31, 2019
  • Accepted : November 27, 2019
  • Published : November 29, 2019

Togtokh Gantur 1 KIM KYUNG CHANG 2

1Ulaan Baatar University
2홍익대학교

Accredited

ABSTRACT

Due to the exponential growth of access information on the web, the need for predicting web users’ next access has increased. Various models such as markov models, deep neural networks, support vector machines, and fuzzy inference models were proposed to handle web access prediction. For deep learning based on neural network models, training time on large-scale web usage data is very huge. To address this problem, deep neural network models are trained on cluster of computers in parallel. In this paper, we investigated impact of several important spark parameters related to data partitions, shuffling, compression, and locality (basic spark parameters) for training Multi-Layer Perceptron model on Spark standalone cluster. Then based on the investigation, we tuned basic spark parameters for training Multi-Layer Perceptron model and used it for tuning Spark when training Multi-Layer Perceptron model for web access prediction. Through experiments, we showed the accuracy of web access prediction based on our proposed web access prediction model. In addition, we also showed performance improvement in training time based on our spark basic parameters tuning for training Multi-Layer Perceptron model over default spark parameters configuration.

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