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@article{ART002524863},
author={Togtokh Gantur and KIM KYUNG CHANG},
title={Web access prediction based on parallel deep learning},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2019},
volume={24},
number={11},
pages={51-59},
doi={10.9708/jksci.2019.24.11.051}

TY - JOUR
AU - Togtokh Gantur
AU - KIM KYUNG CHANG
TI - Web access prediction based on parallel deep learning
JO - Journal of The Korea Society of Computer and Information
PY - 2019
VL - 24
IS - 11
PB - The Korean Society Of Computer And Information
SP - 51
EP - 59
SN - 1598-849X
AB - 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.
KW - Apache Spark;Neural network;Parallel deep learning;Parameter tuning;Web access prediction
DO - 10.9708/jksci.2019.24.11.051
ER -

Togtokh Gantur and KIM KYUNG CHANG. (2019). Web access prediction based on parallel deep learning. Journal of The Korea Society of Computer and Information, 24(11), 51-59.

Togtokh Gantur and KIM KYUNG CHANG. 2019, "Web access prediction based on parallel deep learning", Journal of The Korea Society of Computer and Information, vol.24, no.11 pp.51-59. Available from: doi:10.9708/jksci.2019.24.11.051

Togtokh Gantur, KIM KYUNG CHANG "Web access prediction based on parallel deep learning" Journal of The Korea Society of Computer and Information 24.11 pp.51-59 (2019) : 51.

Togtokh Gantur, KIM KYUNG CHANG. Web access prediction based on parallel deep learning. 2019; 24(11), 51-59. Available from: doi:10.9708/jksci.2019.24.11.051

Togtokh Gantur and KIM KYUNG CHANG. "Web access prediction based on parallel deep learning" Journal of The Korea Society of Computer and Information 24, no.11 (2019) : 51-59.doi: 10.9708/jksci.2019.24.11.051

Togtokh Gantur; KIM KYUNG CHANG. Web access prediction based on parallel deep learning. Journal of The Korea Society of Computer and Information, 24(11), 51-59. doi: 10.9708/jksci.2019.24.11.051

Togtokh Gantur; KIM KYUNG CHANG. Web access prediction based on parallel deep learning. Journal of The Korea Society of Computer and Information. 2019; 24(11) 51-59. doi: 10.9708/jksci.2019.24.11.051

Togtokh Gantur, KIM KYUNG CHANG. Web access prediction based on parallel deep learning. 2019; 24(11), 51-59. Available from: doi:10.9708/jksci.2019.24.11.051

Togtokh Gantur and KIM KYUNG CHANG. "Web access prediction based on parallel deep learning" Journal of The Korea Society of Computer and Information 24, no.11 (2019) : 51-59.doi: 10.9708/jksci.2019.24.11.051