Variational Deep Collaborative Matrix Factorization for Social Recommendation

  • Teng Xiao
  • Hui Tian
  • Hong ShenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11439)


In this paper, we propose a Variational Deep Collaborative Matrix Factorization (VDCMF) algorithm for social recommendation that infers latent factors more effectively than existing methods by incorporating users’ social trust information and items’ content information into a unified generative framework. Unlike neural network-based algorithms, our model is not only effective in capturing the non-linearity among correlated variables but also powerful in predicting missing values under the robust collaborative inference. Specifically, we use variational auto-encoder to extract the latent representations of content and then incorporate them into traditional social trust factorization. We propose an efficient expectation-maximization inference algorithm to learn the model’s parameters and approximate the posteriors of latent factors. Experiments on two sparse datasets show that our VDCMF significantly outperforms major state-of-the-art CF methods for recommendation accuracy on common metrics.


Recommender System Matrix Factorization Deep Learning Generative model 



This work is supported by the National Key Research and Development Program of China (No. #2017YFB0203201) and Australian Research Council Discovery Project DP150104871.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina
  2. 2.School of Information and Communication TechnologyGriffith UniversityGold CoastAustralia
  3. 3.School of Computer ScienceThe University of AdelaideAdelaideAustralia

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