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.
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Xu-na WANG designed the research and drafted the manuscript. Qing-mei TAN helped organize the manuscript. Xu-na WANG revised and finalized the paper.
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Xu-na WANG and Qing-mei TAN declare that they have no conflict of interest.
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Project supported by the National Social Science Foundation of China (No. 19AGL003)
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Wang, Xn., Tan, Qm. DAN: a deep association neural network approach for personalization recommendation. Front Inform Technol Electron Eng 21, 963–980 (2020). https://doi.org/10.1631/FITEE.1900236
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DOI: https://doi.org/10.1631/FITEE.1900236