Improved Collaborative Filtering Method Applied in Movie Recommender System

  • Tian Liang
  • Shunxiang Wu
  • Da Cao
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 146)


Due to the rapid growth of internet, a useful technology named recommender system (RS) become an effective application to make recommendations to users, nowadays, many collaborative recommender systems (CRS) have succeeded in some fields like movies and music web applications; however, there are also some ways for them to be a more effective RS. This paper introduces a new item-based collaborative filtering method which uses mixed similarity, and it also can solve the cold start problem. A series of experiments are accomplished to indicate that the new method can make a better recommendation than the pure item-based collaborative filtering method.


recommender systems collaborative filtering item-based cold start problem mixed similarity 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-Based Collaborative Filtering Recommendation Algorithms. In: Proceedings of the 10th International World Wide Web Conference, pp. 285–295 (2001)Google Scholar
  2. 2.
    Chee, S.H.S., Han, J., Wang, K.: RecTree: An Efficient Collaborative Filtering Method. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2001. LNCS, vol. 2114, p. 141. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  3. 3.
    Kim, B.-M., Li, Q., Kim, J.-W., Kim, J.-S.: A New Collaborative Recommender System Addressing Three Problems. In: Zhang, C., Guesgen, H.W., Yeap, W.-K. (eds.) PRICAI 2004. LNCS (LNAI), vol. 3157, pp. 495–504. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    Antonio, H., Jesus, B., Francisco, S.: Handbook of Social Network Technologies and Applications. Springer, Heidelberg (2004)Google Scholar
  5. 5.
    MovieLens Web Site,
  6. 6.
    McClave, J.T., Dietrich, F.H.: Statistics. Ellen Publishing Company, San Francisco (1998)MATHGoogle Scholar
  7. 7.
    Gupta, D., Digiovanni, M., Narita, H., Goldberg, K.: Jester 2.0: A New Linear-Time Collaborative Filtering Algorithm Applied to Jokes. In: ACM-SIGIR Workshop on Recommender Systems: Algorithms and Evaluation (1999)Google Scholar
  8. 8.
    Qin-hua, H., Wei-min, O.: Fuzzy collaborative filtering with multiple agents. Journal of Shanghai University 11(3), 290–295 (2007)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Fengrong, G., Chunxiao, X., Xiaoyong, D., Shan, W.: Personalized Service System Based on Hybrid Filtering for Digital Library. Tsinghua Science and Technology 12(1), 1–8 (2007)CrossRefGoogle Scholar
  10. 10.
    Kermarrec, A.-M., Leroy, V., Moin, A., Thraves, C.: Application of Random Walks to Decentralized Recommender Systems. In: Lu, C., Masuzawa, T., Mosbah, M. (eds.) OPODIS 2010. LNCS, vol. 6490, pp. 48–63. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of AutomationXiamen UniversityXiamenChina

Personalised recommendations