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A survey of autoencoder-based recommender systems

Abstract

In the past decade, recommender systems have been widely used to provide users with personalized products and services. However, most traditional recommender systems are still facing a challenge in dealing with the huge volume, complexity, and dynamics of information. To tackle this challenge, many studies have been conducted to improve recommender system by integrating deep learning techniques. As an unsupervised deep learning method, autoencoder has been widely used for its excellent performance in data dimensionality reduction, feature extraction, and data reconstruction. Meanwhile, recent researches have shown the high efficiency of autoencoder in information retrieval and recommendation tasks. Applying autoencoder on recommender systems would improve the quality of recommendations due to its better understanding of users’ demands and characteristics of items. This paper reviews the recent researches on autoencoder-based recommender systems. The differences between autoencoder-based recommender systems and traditional recommender systems are presented in this paper. At last, some potential research directions of autoencoder-based recommender systems are discussed.

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Acknowledgments

This work was supported by Beijing Advanced Innovation Center for Future Internet Technology (110000546617001).

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Correspondence to Xiaoning Jin.

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Guijuan Zhang received the BS degree in computer science from Zhengzhou University, China in 2014. She is currently working toward the MS degree in Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, China. Her research interests include recommender system and deep learning.

Yang Liu received the BS degree in network engineering from Tianjin University of Finance and Economics, China in 2016. He is currently working toward the MS degree in the Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, China. His research interests include intelligent recommedation, data mining, and machine learning.

Xiaoning Jin received his BS degree and the PhD degree in information and signal processing with the signal detecting and processing laboratory in the Institute of Acoustics of the Chinese Academy of Sciences, China in 2011. Now he is a lecturer of Beijing University of Technology, China. His current research interests include networking technology, data science, and artificial intelligence.

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Zhang, G., Liu, Y. & Jin, X. A survey of autoencoder-based recommender systems. Front. Comput. Sci. 14, 430–450 (2020). https://doi.org/10.1007/s11704-018-8052-6

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Keywords

  • recommender system
  • autoencoder
  • deep learning
  • data mining