A Novel Hybrid Recommendation System Integrating Content-Based and Rating Information

  • Tan Nghia DuongEmail author
  • Viet Duc Than
  • Tuan Anh Vuong
  • Trong Hiep Tran
  • Quang Hieu Dang
  • Duc Minh Nguyen
  • Hung Manh Pham
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1036)


Collaborative filtering (CF), the most efficient technique in recommendation systems, can be classified into two types: neighborhood-based model and latent factor model. Both are only based on the user-item interaction, or rating information, and do not take into account the item’s content-based information which may contain valuable knowledge. In this work, we propose a hybrid content-based and neighborhood-based recommendation system which utilizes the genome tag associated with each movie in the MovieLens 20M dataset. Experiment results show that our proposed system not only achieves a comparable accuracy but also performs at least 2 times faster than the “pure” CF methods.


Recommendation system Collaborative filtering Similarity measure Neighborhood-based Matrix factorization 



This research is funded by Ministry of Science and Technology (MOST) under grant number 10/2018/ĐTCT-KC.01.14/16-20.


  1. 1.
    Christakopoulou, E., Karypis, G.: HOSLIM: higher-order sparse linear method for top-N recommender systems. In: Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer (2014)Google Scholar
  2. 2.
    Ricci, F., Rokach, L., Shapira, B.: Recommender systems: introduction and challenges. In: Recommender Systems Handbook. Springer (2015)Google Scholar
  3. 3.
    Christakopoulou, E., Karypis, G.: Local item-item models for top-N recommendation. In: Proceedings of the 10th ACM Conference on Recommender Systems. ACM (2016)Google Scholar
  4. 4.
    He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: NAIS: neural attentive item similarity model for recommendation. IEEE Trans. Knowl. Data Eng. 30(12), 2354–2366 (2018)CrossRefGoogle Scholar
  5. 5.
    Nguyen, H.P., Tran, Q.V., Miyoshi, T.: Video compression schemes using edge feature on wireless video sensor networks. Hindawi J. Electr. Comput. Eng. 2012, 27 (2012)Google Scholar
  6. 6.
    Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. In: Proceedings of the 5th ACM Conference on Digital Libraries. ACM (2000)Google Scholar
  7. 7.
    Smith, B., Linden, G.: Two decades of recommender systems at IEEE Internet Comput. 21(3), 12–18 (2017)CrossRefGoogle Scholar
  8. 8.
    Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web (2001)Google Scholar
  9. 9.
    Linden, G., Smith, B., York, J.: recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 1, 76–80 (2003) CrossRefGoogle Scholar
  10. 10.
    Koren, Y., Bell, R.M., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 8, 30–37 (2009)CrossRefGoogle Scholar
  11. 11.
    Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2008)Google Scholar
  12. 12.
    Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining collaborative filtering recommendations. In: Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work. ACM (2000)Google Scholar
  13. 13.
    Tintarev, N., Masthoff, J.: A survey of explanations in recommender systems. In: The IEEE 23rd International Conference on Data Engineering Workshop, ICDE 2007. IEEE (2007)Google Scholar
  14. 14.
    Koren, Y.: Factor in the neighbors: scalable and accurate collaborative filtering. ACM Trans. Knowl. Discov. Data 4, 1 (2010)CrossRefGoogle Scholar
  15. 15.
    Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1999 (1999)Google Scholar
  16. 16.
    Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-based collaborative filtering recommendation algorithms. In: The 10th International Conference on World Wide Web, WWW 2001 (2001)Google Scholar
  17. 17.
    Funk, S.: Netflix Update: Try This At Home (2006).
  18. 18.
    Bell, R.M., Koren, Y.: Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In: 7th IEEE International Conference on Data Mining, ICDM 2007. IEEE (2007)Google Scholar
  19. 19.
    Duong T.N., Than V.D., Tran T.H., Dang Q.H., Nguyen D.M., Pham H.M.: An effective similarity measure for neighborhood-based collaborative filtering. In: Proceedings of the 5th NAFOSTED Conference on Information and Computer Science. IEEE (2018)Google Scholar
  20. 20.
    Harper, F.M., Konstan, J.A.: The MovieLens datasets: history and context. ACM Trans. Interact. Intell. Syst. (TIIS) 5, 19 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Tan Nghia Duong
    • 1
    Email author
  • Viet Duc Than
    • 1
  • Tuan Anh Vuong
    • 1
  • Trong Hiep Tran
    • 1
  • Quang Hieu Dang
    • 1
  • Duc Minh Nguyen
    • 1
  • Hung Manh Pham
    • 1
  1. 1.Hanoi University of Science and TechnologyHanoiVietnam

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