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Neural Netw. 2017 Mar 27;90:83-89. doi: 10.1016/j.neunet.2017.03.009. [Epub ahead of print]

Representation learning via Dual-Autoencoder for recommendation.

Author information

1
Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China. Electronic address: zhuangfz@ics.ict.ac.cn.
2
Beijing University of Posts and Telecommunications, Beijing, China. Electronic address: zzqsmall@gmail.com.
3
Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China. Electronic address: qianmd@ics.ict.ac.cn.
4
Beijing University of Posts and Telecommunications, Beijing, China. Electronic address: shichuan@bupt.edu.cn.
5
Microsoft Research, China. Electronic address: xing.xie@microsoft.com.
6
Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China. Electronic address: heq@ics.ict.ac.cn.

Abstract

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.

KEYWORDS:

Dual-Autoencoder; Matrix factorization; Recommendation; Representation learning

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