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CLC number: TP391

On-line Access: 2020-07-10

Received: 2019-05-10

Revision Accepted: 2019-10-09

Crosschecked: 2020-06-22

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Citations:  Bibtex RefMan EndNote GB/T7714


Xu-na Wang


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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.7 P.963-980


DAN: a deep association neural network approach for personalization recommendation

Author(s):  Xu-na Wang, Qing-mei Tan

Affiliation(s):  College of Economic and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Corresponding email(s):   Xuna@nuaa.edu.cn, tanchina@nuaa.edu.cn

Key Words:  Neural network, Deep learning, Deep association neural network (DAN), Recommendation

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Xu-na Wang, Qing-mei Tan. DAN: a deep association neural network approach for personalization recommendation[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(7): 963-980.

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The collaborative filtering technology used in traditional recommendation systems has a problem of data sparsity. The traditional matrix decomposition algorithm simply decomposes users and items into a linear model of potential factors. These limitations have led to the low accuracy in traditional recommendation algorithms, thus leading to the emergence of recommendation systems based on deep learning. At present, deep learning recommendations mostly use deep neural networks to model some of the auxiliary information, and in the process of modeling, multiple mapping paths are adopted to map the original input data to the potential vector space. However, these deep neural network recommendation algorithms ignore the combined effects of different categories of data, which can have a potential impact on the effectiveness of the recommendation. Aimed at this problem, in this paper we propose a feedforward deep neural network recommendation method, called the deep association neural network (DAN), which is based on the joint action of multiple categories of information, for implicit feedback recommendation. Specifically, the underlying input of the model includes not only users and items, but also more auxiliary information. In addition, the impact of the joint action of different types of information on the recommendation is considered. Experiments on an open data set show the significant improvements made by our proposed method over the other methods. Empirical evidence shows that deep, joint recommendations can provide better recommendation performance.


王旭娜, 谭清美



Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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