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JournalInternational Journal of Computer Applications
TitleUse of Deep Learning in Modern Recommendation System: A Summary of Recent Works
Index TermInformation Systems
AbstractWith the exponential increase in the amount of digital information over the internet, online shops, online music, video and image libraries, search engines and recommendation system have become the most convenient ways to find relevant information within a short time. In the recent times, deep learning’s advances have gained significant attention in the field of speech recognition, image processing and natural language processing. Meanwhile, several recent studies have shown the utility of deep learning in the area of recommendation systems and information retrieval as well. In this short review, we cover the recent advances made in the field of recommendation using various variants of deep learning technology. We organize the review in three parts: Collaborative system, Content based system and Hybrid system. The review also discusses the contribution of deep learning integrated recommendation systems into several application domains. The review concludes by discussion of the impact of deep learning in recommendation system in various domain and whether deep learning has shown any significant improvement over the conventional systems for recommendation. Finally, we also provide future directions of research which are possible based on the current state of use of deep learning in recommendation systems.
KeywordsDeep Learning, Recommender system, Literature review, Machine Learning, Collaborative filtering, Hybrid system.
No. of Pages6
Author NamesAyush Singhal, Pradeep Sinha, Rakesh Pant
  1. A. Singhal and J. Srivastava, “Research dataset discovery from research publications using web context,” Web Intell., vol. 15, no. 2, pp. 81–99, 2017.
  2. A. Singhal, R. Kasturi, and J. Srivastava, “DataGopher: Context-based search for research datasets,” in Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration, IEEE IRI 2014, 2014, pp. 749–756.
  3. A. Singhal, “Leveraging open source web resources to improve retrieval of low text content items,” ProQuest Diss. Theses, p. 161, 2014.
  4. A. Singhal, R. Kasturi, V. Sivakumar, and J. Srivastava, “Leveraging Web intelligence for finding interesting research datasets,” in Proceedings - 2013 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2013, 2013, vol. 1, pp. 321–328.
  5. J. Lu, D. Wu, M. Mao, W. Wang, and G. Zhang, “Recommender system application developments: A survey,” Decis. Support Syst., vol. 74, pp. 12–32, 2015.
  6. S. Lakshmi and T. Lakshmi, “Recommendation Systems: Issues and challenges,” Int. J. Comput. Sci. Inf. Technol., vol. 5, no. 4, pp. 5771–5772, 2014.
  7. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.
  8. A. van den Oord, S. Dieleman, and B. Schrauwen, “Deep content-based music recommendation,” Electron. Inf. Syst. Dep., p. 9, 2013.
  9. X. Wang and Y. Wang, “Improving Content-based and Hybrid Music Recommendation using Deep Learning,” MM, pp. 627–636, 2014.
  10. J. Tan, X. Wan, and J. Xiao, “A Neural Network Approach to Quote Recommendation in Writings,” Proc. 25th ACM Int. Conf. Inf. Knowl. Manag. - CIKM ’16, pp. 65–74, 2016.
  11. H. Lee, Y. Ahn, H. Lee, S. Ha, and S. Lee, “Quote Recommendation in Dialogue using Deep Neural Network,” in Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval - SIGIR ’16, 2016, pp. 957–960.
  12. T. Bansal, D. Belanger, and A. McCallum, “Ask the GRU,” in Proceedings of the 10th ACM Conference on Recommender Systems - RecSys ’16, 2016, pp. 107–114.
  13. L. Zheng, V. Noroozi, and P. S. Yu, “Joint Deep Modeling of Users and Items Using Reviews for Recommendation,” 2017.
  14. X. Wang et al., “Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors’ Demonstration,” in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’17, 2017, pp. 2051–2059.
  15. H. Wang, N. Wang, and D.-Y. Yeung, “Collaborative Deep Learning for Recommender Systems,” in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015, pp. 1235–1244.
  16. S. Li, J. Kawale, and Y. Fu, “Deep Collaborative Filtering via Marginalized Denoising Auto-encoder,” in Proceedings of the 24th ACM International on Conference on Information and Knowledge Management - CIKM ’15, 2015, pp. 811–820.
  17. R. Devooght and H. Bersini, “Collaborative Filtering with Recurrent Neural Networks,” Aug. 2016.
  18. A. K. Balazs Hidasi, “Session-based Recommendation with Recurrent Neural Networks,” ICLR, pp. 1–10, 2016.
  19. B. Hidasi, M. Quadrana, A. Karatzoglou, and D. Tikk, “Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations,” in Proceedings of the 10th ACM Conference on Recommender Systems - RecSys ’16, 2016, pp. 241–248.
  20. D. Jannach and M. Ludewig, “When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation,” in Proceedings of the Eleventh ACM Conference on Recommender Systems - RecSys ’17, 2017, pp. 306–310.
  21. H.-J. Xue, X.-Y. Dai, J. Zhang, S. Huang, and J. Chen, “Deep Matrix Factorization Models for Recommender Systems *,” 2017.
  22. T. Ebesu and Y. Fang, “Neural Semantic Personalized Ranking for item cold-start recommendation,” Inf. Retr. J., vol. 20, no. 2, pp. 109–131, 2017.
  23. S. Cao, N. Yang, and Z. Liu, “Online news recommender based on stacked auto-encoder,” in Proceedings - 16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017, 2017, pp. 721–726.
  24. X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.-S. Chua, “Neural Collaborative Filtering,” in Proceedings of the 26th International Conference on World Wide Web - WWW ’17, 2017, pp. 173–182.
  25. R. Van Den Berg, T. N. Kipf, and M. Welling, “Graph Convolutional Matrix Completion,” arXiv, 2017.
  26. Y. Wu, C. DuBois, A. X. Zheng, and M. Ester, “Collaborative Denoising Auto-Encoders for Top-N Recommender Systems,” in Proceedings of the Ninth ACM International Conference on Web Search and Data Mining - WSDM ’16, 2016, pp. 153–162.
  27. G. Sottocornola, F. Stella, M. Zanker, and F. Canonaco, “Towards a deep learning model for hybrid recommendation,” in Proceedings of the International Conference on Web Intelligence - WI ’17, 2017, pp. 1260–1264.
  28. X. Dong, L. Yu, Z. Wu, Y. Sun, L. Yuan, and F. Zhang, “A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems,” Aaai, pp. 1309–1315, 2017.
  29. D. Kim, C. Park, J. Oh, and H. Yu, “Deep hybrid recommender systems via exploiting document context and statistics of items,” Inf. Sci. (Ny)., vol. 417, pp. 72–87, 2017.
  30. H. Liang and T. Baldwin, “A Probabilistic Rating Auto-encoder for Personalized Recommender Systems,” in Proceedings of the 24th ACM International on Conference on Information and Knowledge Management - CIKM ’15, 2015, pp. 1863–1866.
  31. S. P. Chatzis, P. Christodoulou, and A. S. Andreou, “Recurrent Latent Variable Networks for Session-Based Recommendation,” in Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems - DLRS 2017, 2017, pp. 38–45.
  32. V. Bogina and T. Kuflik, “Incorporating dwell time in session-based recommendations with recurrent Neural networks,” in CEUR Workshop Proceedings, 2017, vol. 1922, pp. 57–59.
  33. D. Kim, C. Park, J. Oh, S. Lee, and H. Yu, “Convolutional Matrix Factorization for Document Context-Aware Recommendation,” in Proceedings of the 10th ACM Conference on Recommender Systems - RecSys ’16, 2016, pp. 233–240.
  34. V. Kumar, D. Khattar, S. Gupta, and M. Gupta, “Deep Neural Architecture for News Recommendation,” in Working Notes of the 8th International Conference of the CLEF Initiative, Dublin, Ireland. CEUR Workshop Proceedings, 2017.
  35. S. Deng, L. Huang, G. Xu, X. Wu, and Z. Wu, “On Deep Learning for Trust-Aware Recommendations in Social Networks,” IEEE Trans. Neural Networks Learn. Syst., vol. 28, no. 5, pp. 1164–1177, 2017.
  36. D. Ding, M. Zhang, S.-Y. Li, J. Tang, X. Chen, and Z.-H. Zhou, “BayDNN: Friend Recommendation with Bayesian Personalized Ranking Deep Neural Network,” in Conference on Information and Knowledge Management (CIKM), 2017, pp. 1479–1488.
  37. Z. Xu, C. Chen, T. Lukasiewicz, and Y. Miao, “Hybrid Deep-Semantic Matrix Factorization for Tag-Aware Personalized Recommendation,” Aug. 2017.
  38. B. Bai, Y. Fan, W. Tan, and J. Zhang, “DLTSR: A Deep Learning Framework for Recommendation of Long-tail Web Services,” IEEE Trans. Serv. Comput., pp. 1–1, 2017.
  39. H. Soh, S. Sanner, M. White, and G. Jamieson, “Deep Sequential Recommendation for Personalized Adaptive User Interfaces,” in Proceedings of the 22nd International Conference on Intelligent User Interfaces - IUI ’17, 2017, pp. 589–593.
  40. Z. Xu, C. Chen, T. Lukasiewicz, Y. Miao, and X. Meng, “Tag-Aware Personalized Recommendation Using a Deep-Semantic Similarity Model with Negative Sampling,” in Proceedings of the 25th ACM International on Conference on Information and Knowledge Management - CIKM ’16, 2016, pp. 1921–1924.

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