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Reseach Article

Deep Learning Methods on Recommender System: A Survey of State-of-the-art

by Basiliyos Tilahun Betru, Charles Awono Onana, Bernabe Batchakui
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 162 - Number 10
Year of Publication: 2017
Authors: Basiliyos Tilahun Betru, Charles Awono Onana, Bernabe Batchakui

Basiliyos Tilahun Betru, Charles Awono Onana, Bernabe Batchakui . Deep Learning Methods on Recommender System: A Survey of State-of-the-art. International Journal of Computer Applications. 162, 10 ( Mar 2017), 17-22. DOI=10.5120/ijca2017913361

@article{ 10.5120/ijca2017913361,
author = { Basiliyos Tilahun Betru, Charles Awono Onana, Bernabe Batchakui },
title = { Deep Learning Methods on Recommender System: A Survey of State-of-the-art },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2017 },
volume = { 162 },
number = { 10 },
month = { Mar },
year = { 2017 },
issn = { 0975-8887 },
pages = { 17-22 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2017913361 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T00:08:39.339754+05:30
%A Basiliyos Tilahun Betru
%A Charles Awono Onana
%A Bernabe Batchakui
%T Deep Learning Methods on Recommender System: A Survey of State-of-the-art
%J International Journal of Computer Applications
%@ 0975-8887
%V 162
%N 10
%P 17-22
%D 2017
%I Foundation of Computer Science (FCS), NY, USA

The advancement in technology accelerated and opened availability of various alternatives to make a choice in every domain. In the era of big data it is a tedious and time consuming task to evaluate the features of a large amount of information provided to make a choice. One solution to ease this overload problem is building recommender system that can process a large amount of data and support users’ decision making ability. In this paper different traditional recommendation techniques, deep learning approaches for recommender system and survey of deep learning techniques on recommender system are presented. A variety of techniques have been proposed to perform recommendation, including content based, collaborative and hybrid recommenders. Due to the limitation of the traditional recommendation methods in obtaining accurate result a deep learning approach is introduced both for collaborative and content based approaches that will enable the model to learn different features of users and items automatically to improve accuracy of recommendation. Even though deep learning poses a great impact in various areas, applying the model to a recommender systems have not been fully exploited. With the help of the advantage of deep learning in modeling different types of data, deep recommender systems can better understand users’ demand to further improve quality of recommendation.

  1. Alexander Tuzhilin, Gediminas Adomavicius. 2005. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions
  2. Andrew I Schein, Alexandrin Popescul, Lyle H Ungar, and David M Pennock. 2002. Methods and metrics for cold-start recommendations. ACM.
  3. Bengio Y. 2009. Learning deep architectures for AI. Foundations and Trends in Machine Learning
  4. Bengio, Y., Ducharme, R., Vincent, P. and Jauvin. 2000. A neural probabilistic language model. NIPS.
  5. Bigdeli, E., and Bahmani, Z. 2008. Comparing accuracy of cosine-based similarity and correlation-based similarity algorithms in tourism recommender systems. In Management of Innovation and Technology.
  6. Bin Shen, Xiaoyuan Su, Russell Greiner, Petr Musilek, and Corrine Cheng. 2003. Discriminative parameter learning of general bayesian network classifiers. In Tools with Artificial Intelligence, IEEE
  7. C. Basu, H. Hirsh, and W. Cohen. 1998. Recommendation as classification: using social and content-based information in recommendation. AAAI.
  8. Chong Wang and David M Blei. 2011. Collaborative topic modeling for recommending scientific articles. International conference on Knowledge discovery and data mining. ACM.
  9. Cicero Nogueira dos Santos, Bing Xiang, and Bowen Zhou. 2015. Classifying relations by ranking with convolutional neural networks.
  10. D. Agarwal and B.-C. Chen. 2009. Regression-based latent factor models. In KDD.
  11. F.O. Isinkayea, Y.O. Folajimib, B.A. Ojokohc. 2015. Recommendation systems: Principles, methods and evaluation Egyptian Informatics Journal.
  12. Fandong Meng, Zhengdong Lu, Mingxuan Wang, Hang Li, Wenbin Jiang, and Qun Liu.2015. Encoding source language with convolutional neural network for machine translation.
  13. G. Hinton, S. Osindero, and Y. The. 2006. A fast learning algorithm for deep belief nets. Neural Computation.
  14. G. Salton and M. McGill. 1983. Introduction to Modern Information Retrieval, McGraw-Hill.
  15. Gediminas Adomavicius and Alexander Tuzhilin. 2005. toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans.
  16. Geoffrey E Hinton and Ruslan R Salakhutdinov. 2006. Reducing the dimensionality of data with neural networks.
  17. Goffery, Simon and Yee. 2006. A fast learning algorithm for DB nets
  18. Graham W Taylor, Geoffrey E Hinton, and Sam T Roweis. 2006. Modeling human motion using binary latent variables. In Advances in neural information processing systems,
  19. Hanna M Wallach.2006. Topic modeling: beyond bag-of words. ACM.
  20. Hanna M Wallach.2006. Topic modeling: beyond bag-of-words. International conference on Machine learning. ACM.
  21. Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015. Collaborative deep learning for recommender systems. ACM.
  22. Heigold, G., Ney, H., Lehnen, P., Gass, T., Schluter, R. 2011. Equivalence of generative and log-liner models. IEEE.
  23. J. Breese, D. Heckerman, and C. Kadie. 1998. Empirical analysis of predictive algorithms for collaborative filtering. Uncertainty in Artificial Intelligence.
  24. J.S. Breese, D.Heckerman, and C.Kadie. 1998. Empirical analysis of predictive algorithms for collaborative filtering. On Uncertainty in Artificial Intelligence.
  25. Joseph Turian, Lev Ratinov, and Yoshua Bengio. 2010. Word representations: a simple and general method for semi-supervised learning. Computational Linguistics.
  26. K. Lang 1995. Newsweeder learning to filter netnews. In ICML.
  27. Koji Miyahara and Michael J Pazzani. 2000. Collaborative filtering with the simple Bayesian classifier. In Topics in Artificial Intelligence.Springer.
  28. L. Hu, J. Cao, G. Xu, L. Cao, Z. Gu, and C. Zhu. 2013. Personalized recommendation via cross-domain triadic factorization. In WWW.
  29. LeCun Y., Chopra S., Ranzato, M., and Huang, F. 2007. Energy-based models in document recognition and computer vision. ICDAR.
  30. Lee, Roger,Pangenstl and dra. 2009. CDBN for scalable unsupervised learning of hierarchical representation.
  31. Lei zheng. 2016. A survey and critique of deep learning on recommender systems. Department of computer science.
  32. Li Deng, Dong Yu. 2014. Deep Learning: Methods and Applications. Foundations and Trends in Signal Processing
  33. M. J. Pazzani. 1999. A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review.
  34. M. K. Condiff, D. D. Lewis, D. Madigan, and C. Posse. 1999. Bayesian mixed-effects models for recommender systems. Recommender Systems: Algorithm and Evaluation.
  35. M. Pazzani and D. Billsus. 1997. Learning and revising user profiles: the identification of interesting web sites. Machine Learning.
  36. Nal Kalchbrenner, Edward Grefenstette, and Phil Blunsom. 2014. A convolutional neural network for modelling sentences.
  37. Nymphia, SatishKumar. 2016. Survey on Content Based Recommendation System. Department of Information Technology. University of Mumbai PIIT.
  38. Oliver, N., Garg, A., and Horvitz, E. 2004. Layered Representations for Learning and Inferring Office Activity from Multiple Sensory Channels. Computer Vision and Image Understanding.
  39. P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. 1994. An open architecture for collaborative filtering of netnews. ACM on Computer Supported Cooperative Work.
  40. Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and PierreAntoine Manzagol. 2010. Stacked denoising autoencoders, learning useful representations in a deep network with a local denoising criterion. The Journal of Machine Learning Research.
  41. Poonam B. Thorat, R. M. Goudar, Sunita Barve. 2015. Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System, International Journal of Computer Applications.
  42. R. Salakhutdinov and A. Mnih. 2007. Probabilistic matrix factorization. In NIPS.
  43. Recommender systems A Computer Science Comprehensive Exercise Carleton College, Northfield, MN.
  44. Robin Burke. 2002. Hybrid Recommender Systems: Survey and Experiments. DePaul University
  45. Ruslan Salakhutdinov, Andriy Mnih, and Geoffrey Hinton. 2007. Restricted Boltzmann machines for collaborative filtering. ACM. Machine learning.
  46. S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In UAI.
  47. T. Chen, W. Zhang, Q. Lu, K. Chen, Z. Zheng, and Y. Yu. 2012. Svd feature: a toolkit for feature-based collaborative filtering. JMLR.
  48. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean.2013. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems.
  49. Wen-tau Yih, Xiaodong He, and Christopher Meek. 2014. Semantic parsing for singlerelation question answering. In Proceedings of ACL, 2014.
  50. Xiaoyuan Su and Taghi M. Khoshgoftaar. 2009. A Survey of Collaborative Filtering Techniques. Department of Computer Science and Engineering, Florida Atlantic University.
  51. Xinxi Wang and Ye Wang. 2014. Improving content based and hybrid music recommendation using deep learning. ACM on Multimedia.
  52. Y. Bengio. 2009. Learning deep architectures for AI. Foundations and Trends in Machine Learning.
  53. Yann LeCun, Y-Lan, Jie, Fu. 2007. Unsupervised learning of invariant feature hierarchical with application to object recognition.
  54. Yann LeCun. 1998. Gradient based learning applied to document recognition
  55. Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems.
  56. Yoshua Bengio. 2007. Learning deep architectures for AI. Technical report, Dept. IRO, Université de Montreal.
Index Terms

Computer Science
Information Sciences


Recommender system deep learning big data decision making collaborative filtering hybrid recommender.