A survey of autoencoder-based recommender systems

doi:10.1007/s11704-018-8052-6

Frontiers of Computer Science

2020, Vol. 14

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

本期目录

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

全文: PDF(642 KB)

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.

Key words： recommender system autoencoder deep learning data mining

收稿日期: 2018-02-12 出版日期: 2019-10-16

Corresponding Author(s): Xiaoning JIN

引用本文:

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

链接本文:

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

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