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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

Cited: 0

Clicked: 686

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Xu-na Wang

https://orcid.org/0000-0002-5882-9562

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

http://doi.org/10.1631/FITEE.1900236


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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Abstract: 
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.

DAN:一种用于个性化推荐的深度联合神经网络

王旭娜, 谭清美
南京航空航天大学经济与管理学院,中国南京市,211106

摘要:传统推荐系统采用的协同过滤技术存在数据稀疏问题,同时传统的矩阵分解算法简单地将用户和项目分解为潜在因素的线性模型,这些局限性导致传统推荐算法推荐效果有限。在此情况下,出现了基于深度学习的推荐系统。当前深度学习推荐大多利用深度神经网络针对一些辅助信息建模,且在建模过程中根据输入数据类别,分别采用多条映射通路,将原始输入数据映射到潜在向量空间。然而,这些深度神经网络推荐算法忽略了不同类别数据间的联合作用可能对推荐效果产生的潜在影响。针对这一问题,本文提出一种基于多类别信息联合作用的前馈深度神经网络推荐方法--深度联合网络,以解决隐性反馈的推荐问题。具体来说,一方面,本文研究在模型的底层输入中不仅包含用户和项目信息,而且包含更多辅助信息。另一方面,充分考虑不同类别信息的联合作用对推荐效果的影响。在公开数据集上的实验表明,我们提出的方法对现有方法有显著改进。经验证据表明,使用深度联合推荐可以提供更好推荐性能。

关键词:神经网络;深度学习;DAN;推荐

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