A survey of autoencoder-based recommender systems

作     者：Guijuan ZHANG Yang LIU Xiaoning JIN Guijuan ZHANG;Yang LIU;Xiaoning JIN

作者机构：Beijing Advanced Innovation Center for Future Internet TechnologyBeijing University of TechnologyBeijing 100124China 

出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)

年 卷 期：2020年第14卷第2期

页      面：430-450页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by Beijing Advanced Inno vation Center for Future Internet Technology(110000546617001) 

主　　题：recommender system autoencoder deep learning data mining 

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