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Multi-view fusion for recommendation with attentive deep neural network

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Abstract

Recommendation systems have been widely developed and introduced in numerous applications. However, due to the lack of sufficient user feedback data, the recommendation performance of such systems is often affected by data sparsity. To address this problem, a multi-view fusion recommendation algorithm with an attentive deep neural network is proposed. A two-stage joint learning solution is designed in the proposed model, which combines user attributes, item attributes, and user-item interaction information into a unified framework. The convolutional neural network and attention mechanism are applied to improve effect of extracting features from user and item attributes. The extended deep neural network model based on matrix factorization and multiple-layer perception is used to enhance the feature extraction of user and item interaction information. Experimental results on the MovieLens-1 M and Book-Crossing real datasets show that the proposed algorithm can achieve the best recommendation accuracy compared with other classical recommendation algorithms, even with extremely sparse data.

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Availability of data and material

Data transparency. https://grouplens.org/datasets/movielens/(ml-1m.zip).

Code availability

Software application or custom code. https://github.com/smilerAI/DNN_AE-AM.

Notes

  1. https://keras.io.

  2. https://grouplens.org/datasets/movielens/.

  3. http://www2.informatik.uni-freiburg.de/~cziegler/BX/.

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Acknowledgements

This paper is supported by National Natural Science Foundation of China (61902439), the basic and applied basic research fund of Guangdong Province (2019A1515012048), 2019 Scientific Research Projects for Special Talents of the Open University of Guangdong (2019-48), The Major Science and Technology Research Programs of Zhongshan City (2019B2006, 2019A40027), Characteristic Innovative projects for young talents of Education Department of Guangdong Province (2019KQNCX223), Characteristic innovation project of Education Department of Guangdong Province (2020KTSCX400).

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Authors and Affiliations

  1. School of Intelligence, The Open University of Guangdong, Guangzhou, Guangdong, China

    Wang Jing & Liu Lei

  2. School of Computing Science and Engineering, Vellore Institute of Technology, Vellore, India

    Arun Kumar Sangaiah

  3. School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, Guangdong, China

    Liu Wei

  4. School of Computer Science, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan, Guangdong, China

    Liu Shaopeng & Liang Ruishi

Authors
  1. Wang Jing
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  2. Arun Kumar Sangaiah
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  3. Liu Wei
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  4. Liu Shaopeng
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  5. Liu Lei
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  6. Liang Ruishi
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Contributions

WJ: Methodology, Experiments, Writing; AKS: Methodology, Reviewing; LW: Theory, Model optimization; LS: Reviewing; LL: Experiments; Liang Ruishi: Revising.

Corresponding author

Correspondence to Arun Kumar Sangaiah.

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Jing, W., Sangaiah, A.K., Wei, L. et al. Multi-view fusion for recommendation with attentive deep neural network. Evol. Intel. 15, 2619–2629 (2022). https://doi.org/10.1007/s12065-021-00626-6

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