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Representation learning via Dual-Autoencoder for recommendation

文献类型：期刊论文

作者Zhuang, Fuzhen1; Zhang, Zhiqiang2; Qian, Mingda1; Shi, Chuan2; Xie, Xing3; He, Qing1
刊名NEURAL NETWORKS
出版日期2017-06-01
卷号90页码:83-89
关键词Matrix factorization Dual-Autoencoder Recommendation Representation learning
ISSN号0893-6080
DOI10.1016/j.neunet.2017.03.009
英文摘要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.
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
语种英语
WOS记录号WOS:000402354900008
出版者PERGAMON-ELSEVIER SCIENCE LTD
源URL[http://119.78.100.204/handle/2XEOYT63/7130]  
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.

入库方式： OAI收割

来源：计算技术研究所

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