Hybrid Collaborative Recommendation via Semi-AutoEncoder

  • Shuai Zhang
  • Lina Yao
  • Xiwei Xu
  • Sen Wang
  • Liming Zhu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10634)


In this paper, we present a novel structure, Semi-AutoEncoder, based on AutoEncoder. We generalize it into a hybrid collaborative filtering model for rating prediction as well as personalized top-n recommendations. Experimental results on two real-world datasets demonstrate its state-of-the-art performances.


Recommender systems Semi-AutoEncoder Collaborative filtering 


  1. 1.
    Zhang, S., Yao, L., Sun, A.: Deep learning based recommender system: a survey and new perspectives. arXiv preprint arXiv:1707.07435 (2017)
  2. 2.
    Li, S., Kawale, J., Fu, Y.: Deep collaborative filtering via marginalized denoising auto-encoder. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. CIKM 2015, pp. 811–820. ACM, New York (2015)Google Scholar
  3. 3.
    Zhang, S., Yao, L., Xu, X.: Autosvd++: an efficient hybrid collaborative filtering model via contractive auto-encoders. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR 2017, pp. 957–960. ACM, New York (2017)Google Scholar
  4. 4.
    Sedhain, S., Menon, A.K., Sanner, S., Xie, L.: Autorec: autoencoders meet collaborative filtering. In: Proceedings of the 24th International Conference on World Wide Web. WWW 2015 Companion, pp. 111–112. ACM, New York (2015)Google Scholar
  5. 5.
    Ouyang, Y., Liu, W., Rong, W., Xiong, Z.: Autoencoder-based collaborative filtering. In: Loo, C.K., Yap, K.S., Wong, K.W., Beng Jin, A.T., Huang, K. (eds.) ICONIP 2014. LNCS, vol. 8836, pp. 284–291. Springer, Cham (2014). doi: 10.1007/978-3-319-12643-2_35 Google Scholar
  6. 6.
    Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016).
  7. 7.
    Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Ng, A.Y.: Multimodal deep learning. In: Proceedings of the 28th International Conference on Machine Learning. ICML 2011, pp. 689–696. ACM, New York (2011)Google Scholar
  8. 8.
    Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook, pp. 1–35. Springer, Boston (2011). doi: 10.1007/978-0-387-85820-3_1 CrossRefGoogle Scholar
  9. 9.
    Wang, H., Wang, N., Yeung, D.Y.: Collaborative deep learning for recommender systems. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD 2015, pp. 1235–1244. ACM, New York (2015)Google Scholar
  10. 10.
    Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: bayesian personalized ranking from implicit feedback. In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. UAI 2009, pp. 452–461. AUAI Press, Arlington (2009)Google Scholar
  11. 11.
    Ning, X., Karypis, G.: Slim: Sparse linear methods for top-n recommender systems. In: 2011 IEEE 11th International Conference on Data Mining, pp. 497–506 (2011)Google Scholar
  12. 12.
    Salakhutdinov, R., Mnih, A., Hinton, G.: Restricted boltzmann machines for collaborative filtering. In: Proceedings of the 24th International Conference on Machine Learning. ICML 2007, pp. 791–798. ACM, New York (2007)Google Scholar
  13. 13.
    Strub, F., Gaudel, R., Mary, J.: Hybrid recommender system based on autoencoders. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. DLRS 2016, pp. 11–16. ACM, New York (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Shuai Zhang
    • 1
  • Lina Yao
    • 1
  • Xiwei Xu
    • 2
  • Sen Wang
    • 3
  • Liming Zhu
    • 2
  1. 1.School of Computer Science and EngineeringUniversity of New South WalesKensingtonAustralia
  2. 2.Data61, CSIROSydneyAustralia
  3. 3.School of Information and Communication TechnologyGriffith UniversityNathanAustralia

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