Recommendation system using Deep Autoencoder for Tensor data

Jina Park; Hwanseung Yong

doi:https://doi.org/10.9708/jksci.2019.24.08.087

Journal of The Korea Society of Computer and Information

Abbr : JKSCI
2019, 24(8), pp.87-93
DOI :https://doi.org/10.9708/jksci.2019.24.08.087
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science

Jina Park ¹ , Hwanseung Yong ²

¹ 이화여자대학교
² 이화여자대학교

ABSTRACT

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.
KEYWORDS

Deep Autoencoder , Recommendation system , Collaborative filtering , Tensor data
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KCI Citation (0)

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