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Bayesian dual neural networks for recommendation

  • Jia He
  • Fuzhen ZhuangEmail author
  • Yanchi Liu
  • Qing He
  • Fen Lin
Research Article
  • 12 Downloads

Abstract

Most traditional collaborative filtering (CF) methods only use the user-item rating matrix to make recommendations, which usually suffer from cold-start and sparsity problems. To address these problems, on the one hand, some CF methods are proposed to incorporate auxiliary information such as user/item profiles; on the other hand, deep neural networks, which have powerful ability in learning effective representations, have achieved great success in recommender systems. However, these neural network based recommendation methods rarely consider the uncertainty of weights in the network and only obtain point estimates of the weights. Therefore, they maybe lack of calibrated probabilistic predictions and make overly confident decisions. To this end, we propose a new Bayesian dual neural network framework, named BDNet, to incorporate auxiliary information for recommendation. Specifically, we design two neural networks, one is to learn a common low dimensional space for users and items from the rating matrix, and another one is to project the attributes of users and items into another shared latent space. After that, the outputs of these two neural networks are combined to produce the final prediction. Furthermore, we introduce the uncertainty to all weights which are represented by probability distributions in our neural networks to make calibrated probabilistic predictions. Extensive experiments on real-world data sets are conducted to demonstrate the superiority of our model over various kinds of competitors.

Keywords

collaborative filtering Bayesian neural network hybrid recommendation algorithm 

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Notes

Acknowledgements

The research work was supported by the National Key R&D Program of China (2018YFB1004300), the National Natural Science Foundation of China (Grant Nos. 61773361, 61473273, 91546122), the Science and Technology Project of Guangdong Province (2015B010109005), the Project of Youth Innovation Promotion Association CAS (2017146). This work was also partly supported by the funding of WeChat cooperation project. We thank Bo Chen, Leyu Lin, Cheng Niu, Xiaohu Cheng for their constructive advices.

Supplementary material

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Supplementary material, approximately 382 KB.

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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Jia He
    • 1
    • 2
  • Fuzhen Zhuang
    • 1
    • 2
    Email author
  • Yanchi Liu
    • 3
  • Qing He
    • 1
    • 2
  • Fen Lin
    • 4
  1. 1.Key Lab of Intelligent Information Processing of Chinese Academy of Sciences, Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Rutgers UniversityNewarkUSA
  4. 4.Search Product Center, WeChat Search Application DepartmentTencentBeijingChina

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