@article{ART002495850},
author={Jina Park and Hwanseung Yong},
title={Recommendation system using Deep Autoencoder for Tensor data},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2019},
volume={24},
number={8},
pages={87-93},
doi={10.9708/jksci.2019.24.08.087}
TY - JOUR
AU - Jina Park
AU - Hwanseung Yong
TI - Recommendation system using Deep Autoencoder for Tensor data
JO - Journal of The Korea Society of Computer and Information
PY - 2019
VL - 24
IS - 8
PB - The Korean Society Of Computer And Information
SP - 87
EP - 93
SN - 1598-849X
AB - These days, as interest in the recommendation system with deep learning is increasing, a number of related studies to develop a performance for collaborative filtering through autoencoder, a state-of-the-art deep learning neural network architecture has advanced considerably. The purpose of this study is to propose autoencoder which is used by the recommendation system to predict ratings, and we added more hidden layers to the original architecture of autoencoder so that we implemented deep autoencoder with 3 to 5 hidden layers for much deeper architecture. In this paper, therefore we make a comparison between the performance of them. In this research, we use 2-dimensional arrays and 3-dimensional tensor as the input dataset. As a result, we found a correlation between matrix entry of the 3-dimensional dataset such as item-time and user-time and also figured out that deep autoencoder with extra hidden layers generalized even better performance than autoencoder.
KW - Deep Autoencoder;Recommendation system;Collaborative filtering;Tensor data
DO - 10.9708/jksci.2019.24.08.087
ER -
Jina Park and Hwanseung Yong. (2019). Recommendation system using Deep Autoencoder for Tensor data. Journal of The Korea Society of Computer and Information, 24(8), 87-93.
Jina Park and Hwanseung Yong. 2019, "Recommendation system using Deep Autoencoder for Tensor data", Journal of The Korea Society of Computer and Information, vol.24, no.8 pp.87-93. Available from: doi:10.9708/jksci.2019.24.08.087
Jina Park, Hwanseung Yong "Recommendation system using Deep Autoencoder for Tensor data" Journal of The Korea Society of Computer and Information 24.8 pp.87-93 (2019) : 87.
Jina Park, Hwanseung Yong. Recommendation system using Deep Autoencoder for Tensor data. 2019; 24(8), 87-93. Available from: doi:10.9708/jksci.2019.24.08.087
Jina Park and Hwanseung Yong. "Recommendation system using Deep Autoencoder for Tensor data" Journal of The Korea Society of Computer and Information 24, no.8 (2019) : 87-93.doi: 10.9708/jksci.2019.24.08.087
Jina Park; Hwanseung Yong. Recommendation system using Deep Autoencoder for Tensor data. Journal of The Korea Society of Computer and Information, 24(8), 87-93. doi: 10.9708/jksci.2019.24.08.087
Jina Park; Hwanseung Yong. Recommendation system using Deep Autoencoder for Tensor data. Journal of The Korea Society of Computer and Information. 2019; 24(8) 87-93. doi: 10.9708/jksci.2019.24.08.087
Jina Park, Hwanseung Yong. Recommendation system using Deep Autoencoder for Tensor data. 2019; 24(8), 87-93. Available from: doi:10.9708/jksci.2019.24.08.087
Jina Park and Hwanseung Yong. "Recommendation system using Deep Autoencoder for Tensor data" Journal of The Korea Society of Computer and Information 24, no.8 (2019) : 87-93.doi: 10.9708/jksci.2019.24.08.087
