Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Recommendation system using Deep Autoencoder for Tensor data

  • Park, Jina (Dept. of Computer Science & Engineering, Graduate School, Ewha Womans University) ;
  • Yong, Hwan-Seung (Dept. of Computer Science & Engineering, Ewha Womans University)
  • Received : 2019.06.18
  • Accepted : 2019.08.06
  • Published : 2019.08.30
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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.

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References

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