Representation learning via Dual-Autoencoder for recommendation

doi:10.1016/j.neunet.2017.03.009

	Representation learning via Dual-Autoencoder for recommendation
	Zhuang, Fuzhen 1; Zhang, Zhiqiang 2; Qian, Mingda 1; Shi, Chuan 2; Xie, Xing 3; He, Qing 1
	2017-06-01
发表期刊	NEURAL NETWORKS
ISSN	0893-6080
卷号	90 页码:83-89
摘要	Recommendation has provoked vast amount of attention and research in recent decades. Most previous works employ matrix factorization techniques to learn the latent factors of users and items. And many subsequent works consider external information, e.g., social relationships of users and items' attributions, to improve the recommendation performance under the matrix factorization framework. However, matrix factorization methods may not make full use of the limited information from rating or check-in matrices, and achieve unsatisfying results. Recently, deep learning has proven able to learn good representation in natural language processing, image classification, and so on. Along this line, we propose a new representation learning framework called Recommendation via Dual-Autoencoder (ReDa). In this framework, we simultaneously learn the new hidden representations of users and items using autoencoders, and minimize the deviations of training data by the learnt representations of users and items. Based on this framework, we develop a gradient descent method to learn hidden representations. Extensive experiments conducted on several real-world data sets demonstrate the effectiveness of our proposed method compared with state-of-the-art matrix factorization based methods. (C) 2017 Elsevier Ltd. All rights reserved.
关键词	Matrix factorization Dual-Autoencoder Recommendation Representation learning
DOI	10.1016/j.neunet.2017.03.009
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61473273] ; National Natural Science Foundation of China[91546122] ; National Natural Science Foundation of China[61573335] ; National Natural Science Foundation of China[61602438] ; Guangdong provincial science and technology plan projects[2015 B010109005] ; Youth Innovation Promotion Association CAS[2017146] ; Microsoft Research Asia Collaborative Research Program
WOS研究方向	Computer Science ; Neurosciences & Neurology
WOS类目	Computer Science, Artificial Intelligence ; Neurosciences
WOS记录号	WOS:000402354900008
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计	被引频次：74[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/7131
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhuang, Fuzhen
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 2.Beijing Univ Posts & Telecommun, Beijing, Peoples R China 3.Microsoft Res, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Zhuang, Fuzhen,Zhang, Zhiqiang,Qian, Mingda,et al. Representation learning via Dual-Autoencoder for recommendation[J]. NEURAL NETWORKS,2017,90:83-89.
APA	Zhuang, Fuzhen,Zhang, Zhiqiang,Qian, Mingda,Shi, Chuan,Xie, Xing,&He, Qing.(2017).Representation learning via Dual-Autoencoder for recommendation.NEURAL NETWORKS,90,83-89.
MLA	Zhuang, Fuzhen,et al."Representation learning via Dual-Autoencoder for recommendation".NEURAL NETWORKS 90(2017):83-89.