Recommendation System Based on Deep Learning

  • Tianhan GaoEmail author
  • Lei JiangEmail author
  • Xibao Wang
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 97)


With the exponential growth of digital resource from Internet, search engines and recommendation systems have become the effective way to find relevant information in a short period of time. In recent years, advances in deep learning have received great attention in the fields of speech recognition, image processing, and natural language processing. The recommendation system is an important technology to alleviate information overload. How to integrate deep learning into the recommendation system, use the advantages of deep learning to learn the inherent essential characteristics of users and items from various complex multi-dimensional data, and build a model that more closely matches the user’s interest needs has become a hotpot in the research field. This paper reviews the research and application status of recommendation algorithms based on deep learning, and tries to discusses and forecasts the research trends of deep learning approaches applied to recommendation systems. proceedings.



This paper is supported by China Fundamental Research Funds for the Central Universities under Grant No. N180716019 and Grant No. N182808003.


  1. 1.
    Linden, G., Smith, B., York, J.: recommendations item-to-item collaborative filtering. IEEE Internet Comput. 1, 76–80 (2003)CrossRefGoogle Scholar
  2. 2.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommeder system. Computer 8, 30–37 (2009) CrossRefGoogle Scholar
  3. 3.
    Kadie, C., Breese, J.S., Heckerman, D.: Empirical analysis of predictive algorithms for collaborative filtering, pp. 43–52 (1998)Google Scholar
  4. 4.
    Roy, L., Mooney R.J.: Content-based book recommending using learning for text categorization, pp. 195–204 (2000)Google Scholar
  5. 5.
    Shohom, Y., Balabanovic, M.: Content-based, collaborative recommendation. Commun. ACM 40(3), 66–72 (1997)CrossRefGoogle Scholar
  6. 6.
    Huang, L., Jiang, B., Lu, S., Liu, Y., Li, D.: Dynamic hybrid recommendation algorithm based on stack noise reduction encoder. Chin. J. Comput. 41(7), 1619–1647 (2018)Google Scholar
  7. 7.
    Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–127 (2009)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Wang, H., Shi, X., Yeung, Y.: Relational stacked denoising autoencoder for tag recommendation. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 3052–3058. AAAI Press, Menlo Park (2015)Google Scholar
  9. 9.
    Hinton, G., Osindero, S., Teh, Y.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Vincent, P., Larochelle, H., Lajoie, I.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11(12), 3371–3408 (2010)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Van den Oord, A., Dieleman, S., Schrauwen, B.: Deep content-based music recommendation. In: Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, USA, pp. 2643–2651 (2013)Google Scholar
  12. 12.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, USA, pp. 1097–1105 (2012)Google Scholar
  13. 13.
    Zhang, F., Yuan, N.J., Lian, D.: Collaborative knowledge base embedding for recommender systems. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, USA, pp. 353–362 (2016)Google Scholar
  14. 14.
    Williams, D., Hinton, G.: Learning representations by back-propagating errors. Nature 323(6088), 533–538 (1986)CrossRefGoogle Scholar
  15. 15.
    Geng, X., Zhang, H., Bian, J.: Learning image and user feature recommendation in social networks. In: Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile, pp. 4274–4282 (2015)Google Scholar
  16. 16.
    Wu, C.Y., Ahmed, A., Beutel, A.: Recurrent recommender networks. In: Proceedings of the 10th ACM International Conference on Web Search and Data Mining, Cambridge, UK, pp. 495–503 (2017)Google Scholar
  17. 17.
    Sedhain, S., Menon, A.K., Sanner, S., Xie, L.: AutoRec: autoencoders meet collaborative filtering. In: WWW 2015 Companion Proceedings of the 24th International Conference on World Wide Web, pp. 111–112 (2015)Google Scholar
  18. 18.
    Wu, Y., DuBois, C., Zheng, A.X., Ester, M.: Collaborative denoising auto-encoders for top-N recommender systems. In: WSDM 2016 Proceedings of the Ninth ACM International Conference on Web Search and Data Mining (2016)Google Scholar
  19. 19.
    Zhang, S., Yao, L., Xu, X.: AutoSVD++: an efficient hybrid collaborative filtering model via contractive auto-encoders. In: SIGIR 2017 Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information (2017)Google Scholar
  20. 20.
    Liang, D., Krishnan, R.G., Hoffman, M.D., Jebara, T.: Variational autoencoders for collaborative filtering. In: WWW 2018 Proceedings of the 2018 World Wide Web Conference (2018)Google Scholar
  21. 21.
    Li, X., She, J.: Collaborative variational autoencoder for recommender systems. In: KDD 2017 Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2007)Google Scholar
  22. 22.
    Covington, P., Adams, J., Sargin, E.: Deep neural networks for Youtube recommendations. In: RecSys 2016-Proceedings of the 10th ACM Conference on Recommender Systems, pp. 191–198 (2016)Google Scholar
  23. 23.
    Wang, J., Zhao, H., Zhang, Z., Zhao, B., Lee, D.L., Huang, P.: Billion-scale commodity embedding for e-commerce recommendation in Alibaba. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 839-848 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Engineering Research Center of Security Technology of Complex Network System, Ministry of Education, Liaoning Research Center of Safety Engineering Technology in Industrial ControlNortheastern UniversityShenyangChina
  2. 2.Northeastern UniversityShenyangChina
  3. 3.Dalian University of TechnologyDalianChina

Personalised recommendations