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Deep Learning Based Approaches for Recommendation Systems

  • Balaji BalasubramanianEmail author
  • Pranshu DiwanEmail author
  • Deepali VoraEmail author
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 38)

Abstract

Recommendation systems are one of the most widely used Machine Learning algorithms in the industry. Deep learning, a branch of machine learning, is popularly used in fields like Computer Vision, Natural Language Processing etc. Recommender systems have started widely using Deep Learning for generation of recommendations. This paper studies different deep learning methods for the recommendation system highlighting the important aspects of design and implementation.

Keywords

Recommendation systems Matrix factorization Collaborative filtering Deep Learning Recurrent Neural Network Convolutional Neural Network 

Notes

Acknowledgement

The authors would like to thank all our anonymous critics for their feedback on this paper, along with the faculty of Vidyalankar Institute of Technology for their unconditional support.

References

  1. 1.
    Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: IEEE International Conference on Data Mining (ICDM 2008), pp. 263–272 (2008)Google Scholar
  2. 2.
    Geuens, S., Coussement, K., De Bock, K.: A framework for configuring collaborative filtering-based recommendations derived from purchase data. Eur. J. Oper. Res. (2017).  https://doi.org/10.1016/j.ejor.2017.07.005CrossRefzbMATHGoogle Scholar
  3. 3.
    He, X., Zhang, H., Kan, M.-Y., Chua, T.-S.: Fast matrix factorization for online recommendation with implicit feedback. In: SIGIR, pp. 549–558 (2016)Google Scholar
  4. 4.
    Wang, H., Wang, N., Yeung, D.-Y.: Collaborative deep learning for recommender systems. In: KDD, pp. 1235–1244 (2015)Google Scholar
  5. 5.
    Najafabadi, M.K., Mahrin, M.N., Chuprat, S., Sarkan, H.M.: Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit data. Comput. in Hum. Behav. 67(Supplement C), 113–128 (2017)CrossRefGoogle Scholar
  6. 6.
    Seo, J.D.: Matrix Factorization Techniques for Recommender Systems. Towards Data Science, 22 November 2018. https://towardsdatascience.com/paper-summary-matrix-factorization-techniques-for-recommender-systems-82d1a7ace74
  7. 7.
    Fridman, L., Brown, D.E., Glazer, M., Angell, W., Dodd, S., Jenik, B., Terwilliger, J., Kindelsberger, J., Ding, L., Seaman, S., et al.: Mit autonomous vehicle technology study: Large-scale deep learning based analysis of driver behavior and interaction with automation, arXiv preprint arXiv:1711.06976 (2017)
  8. 8.
    Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. CoRR abs/1409.0473 (2014). http://arxiv.org/abs/1409.0473
  9. 9.
    Amodei, D., Anubhai, R., Battenberg, E., Case, C., Casper, J., Catanzaro, B., Chen, J., Chrzanowski, M., Coates, A., Diamos, G., et al.: Deep speech 2: end-to-end speech recognition in English and Mandarin, pp. 173–182 (2016)Google Scholar
  10. 10.
    Rendle, S.: Factorization machines. In: ICDM, pp. 995–1000 (2010)Google Scholar
  11. 11.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  12. 12.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)Google Scholar
  13. 13.
    Berg, A., Deng, J., Fei-Fei, L.L.: Large scale visual recognition challenge 2010. www.image-net.org/challenges. (2010)
  14. 14.
    Pouyanfar, S., Sadiq, S., Yan, Y., Tian, H., Tao, Y., Reyes, M.P., Iyengar, S.S.: A survey on deep learning: algorithms, techniques, and applications. ACM Comput. Surv. (CSUR) 51(5), 92 (2018)CrossRefGoogle Scholar
  15. 15.
    Jordan, M.I.: Serial order: a parallel distributed processing approach. Adv. Psychol. 121(1986), 471–495 (1986)Google Scholar
  16. 16.
    Socher, R., Perelygin, A., Wu, J.Y., Chuang, J., Christopher, D.M., Ng, A.Y., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Conference on Empirical Methods in Natural Language Processing. Citeseer, Association for Computational Linguistics, pp. 1631–1642 (2013)Google Scholar
  17. 17.
    Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. CoRR abs/1409.0473 (2014). http://arxiv.org/abs/1409.0473
  18. 18.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  19. 19.
    Cho, K., van Merrienboer, B., Gülçehre, Ç., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: The Conference on Empirical Methods in Natural Language Processing, pp. 1724–1734 (2014)Google Scholar
  20. 20.
    Fu, R., Zhang, Z., Li, L.: Using lstm and gru neural network methods for traffic flow prediction. In: 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), pp. 324–328, November 2016Google Scholar
  21. 21.
    Oord, A.V.D., Dieleman, S., Schrauwen, B.: Deep content-based music recommendation. In: NIPS, pp. 2643 − 2651 (2013)Google Scholar
  22. 22.
    Bertin-Mahieux, T., Ellis, D. P.W., Whitman, B., Lamere, P.: The million song dataset. In: Proceedings of the 11th International Conference on Music Information Retrieval (ISMIR) (2011)Google Scholar
  23. 23.
    Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining (2008)Google Scholar
  24. 24.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015). arXiv:1512.03385
  25. 25.
    Covington, P.; Adams, J., Sargin, E.: Deep neural networks for youtube recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, ACM, pp. 191–198 (2016)Google Scholar
  26. 26.
    Hidasi, F.B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015)
  27. 27.
    Cheng, H.-T., Koc, L., Harmsen, J., et al.: Wide & deep learning for recommender systems. In: 1st Workshop on Deep Learning for RecSys, pp. 7–10 (2016)Google Scholar
  28. 28.
    Nedelec, T., Smirnova, E., Vasile, F.: Specializing joint representations for the task of product recommendation, arXiv preprint arXiv:1706.07625 (2017)
  29. 29.
    Kim, A.Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882. (2014)
  30. 30.
    Guo, I.H., Tang, R. et al.: DeepFM: a factorization-machine based neural network for CTR prediction. In Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 1725–1731 (2017)Google Scholar
  31. 31.
    Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co- adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012)
  32. 32.
    Zhang, Y., Jin, R., Zhou, Z.-H.: Understanding bag-of-words model: a statistical framework. Int. J. Mach. Learn. Cybern. 1(1–4), 43–52 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Information TechnologyVidyalankar Institute of TechnologyMumbaiIndia

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