A survey of autoencoder-based recommender systems

doi:10.1007/s11704-018-8052-6

Front. Comput. Sci.

2020, Vol. 14

Issue (2) : 430-450 https://doi.org/10.1007/s11704-018-8052-6

REVIEW ARTICLE

A survey of autoencoder-based recommender systems

Guijuan ZHANG, Yang LIU, Xiaoning JIN(

)

Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing 100124, China

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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.

Keywords recommender system autoencoder deep learning data mining

Corresponding Author(s): Xiaoning JIN

Just Accepted Date: 20 November 2018 Online First Date: 17 September 2019 Issue Date: 16 October 2019

Cite this article:

Guijuan ZHANG,Yang LIU,Xiaoning JIN. A survey of autoencoder-based recommender systems[J]. Front. Comput. Sci., 2020, 14(2): 430-450.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-018-8052-6
https://academic.hep.com.cn/fcs/EN/Y2020/V14/I2/430

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