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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)

Abstract

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.

Keywords

Recommender System Matrix Factorization Deep Learning Generative model 

Notes

Acknowledgement

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

References

  1. 1.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)zbMATHGoogle Scholar
  2. 2.
    Chen, C., Zheng, X., Wang, Y., Hong, F., Lin, Z., et al.: Context-aware collaborative topic regression with social matrix factorization for recommender systems. In: AAAI, pp. 9–15 (2014)Google Scholar
  3. 3.
    Chen, M., Weinberger, K., Sha, F., Bengio, Y.: Marginalized denoising auto-encoders for nonlinear representations. In: International Conference on Machine Learning, pp. 1476–1484 (2014)Google Scholar
  4. 4.
    Chen, Y., de Rijke, M.: A collective variational autoencoder for top-n recommendation with side information. In: Proceedings of the 3rd Workshop on Deep Learning for Recommender Systems, pp. 3–9. ACM (2018)Google Scholar
  5. 5.
    Croft, W.B., Metzler, D., Strohman, T.: Search Engines: Information Retrieval in Practice. Addison-Wesley, Reading (2015)Google Scholar
  6. 6.
    Dayan, P., Hinton, G.E., Neal, R.M., Zemel, R.S.: The Helmholtz machine. Neural Comput. 7(5), 889–904 (1995)CrossRefGoogle Scholar
  7. 7.
    Doersch, C.: Tutorial on variational autoencoders. CoRR abs/1606.05908 (2016)Google Scholar
  8. 8.
    Gershman, S., Goodman, N.: Amortized inference in probabilistic reasoning. In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol. 36 (2014)Google Scholar
  9. 9.
    He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: WWW, pp. 173–182 (2017)Google Scholar
  10. 10.
    Hu, G.N., et al.: Collaborative filtering with topic and social latent factors incorporating implicit feedback. ACM Trans. Knowl. Discov. Data (TKDD) 12(2), 23 (2018)Google Scholar
  11. 11.
    Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: ICLR (2014)Google Scholar
  12. 12.
    Li, S., Kawale, J., Fu, Y.: Deep collaborative filtering via marginalized denoising auto-encoder. In: CIKM, pp. 811–820 (2015)Google Scholar
  13. 13.
    Li, X., She, J.: Collaborative variational autoencoder for recommender systems. In: KDD, pp. 305–314 (2017)Google Scholar
  14. 14.
    Liang, D., Krishnan, R.G., Hoffman, M.D., Jebara, T.: Variational autoencoders for collaborative filtering. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web, pp. 689–698. International World Wide Web Conferences Steering Committee (2018)Google Scholar
  15. 15.
    Ma, H., Yang, H., Lyu, M.R., King, I.: Sorec: social recommendation using probabilistic matrix factorization. In: CIKM, pp. 931–940 (2008)Google Scholar
  16. 16.
    Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: WSDM, pp. 287–296 (2011)Google Scholar
  17. 17.
    Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. In: NIPS, pp. 1257–1264 (2008)Google Scholar
  18. 18.
    Purushotham, S., Liu, Y., Kuo, C.C.J.: Collaborative topic regression with social matrix factorization for recommendation systems. In: ICML, pp. 691–698 (2012)Google Scholar
  19. 19.
    da Silva, E.S., Langseth, H., Ramampiaro, H.: Content-based social recommendation with poisson matrix factorization. In: Ceci, M., Hollmén, J., Todorovski, L., Vens, C., Džeroski, S. (eds.) ECML PKDD 2017. LNCS (LNAI), vol. 10534, pp. 530–546. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-71249-9_32CrossRefGoogle Scholar
  20. 20.
    Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11(Dec), 3371–3408 (2010)MathSciNetzbMATHGoogle Scholar
  21. 21.
    Wang, C., Blei, D.M.: Collaborative topic modeling for recommending scientific articles. In: KDD, pp. 448–456 (2011)Google Scholar
  22. 22.
    Wang, H., Chen, B., Li, W.J.: Collaborative topic regression with social regularization for tag recommendation. In: IJCAI, pp. 2719–2725 (2013)Google Scholar
  23. 23.
    Wang, H., Shi, X., Yeung, D.Y.: Relational stacked denoising autoencoder for tag recommendation. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)Google Scholar
  24. 24.
    Wang, H., Wang, N., Yeung, D.Y.: Collaborative deep learning for recommender systems. In: KDD, pp. 1235–1244 (2015)Google Scholar
  25. 25.
    Wu, H., Yue, K., Pei, Y., Li, B., Zhao, Y., Dong, F.: Collaborative topic regression with social trust ensemble for recommendation in social media systems. Knowl.-Based Syst. 97, 111–122 (2016)CrossRefGoogle Scholar
  26. 26.
    Zhang, F., Yuan, N.J., Lian, D., Xie, X., Ma, W.Y.: Collaborative knowledge base embedding for recommender systems. In: KDD, pp. 353–362 (2016)Google Scholar

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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