Improving Recommendation Based on Features’ Co-occurrence Effects in Collaborative Tagging Systems

  • Hao Han
  • Yi Cai
  • Yifeng Shao
  • Qing Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7235)


Currently, recommender system becomes more and more important and challenging, as users demand higher recommendation quality. Collaborative tagging systems allow users to annotate resources with their own tags which can reflect users’ attitude on these resources and some attributes of resources. Based on our observation, we notice that there is co-occurrence effect of features, which may cause the change of user’s favor on resources. Current recommendation methods do not take it into consideration. In this paper, we propose an assistant and enhanced method to improve the performance of other methods by combining co-occurrence effect of features in collaborative tagging environment.


Root Mean Square Error Recommender System Vector Space Model Mean Absolute Error Recommendation Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    A Survey of E-Commerce Recommender Systems (June 2007)Google Scholar
  2. 2.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)CrossRefGoogle Scholar
  3. 3.
    Bennett, J., Lanning, S., Netflix, N.: The netflix prize. In: KDD Cup and Workshop in conjunction with KDD (2007)Google Scholar
  4. 4.
    Cai, Y., Leung, H.F., Li, Q., Tang, J., Li, J.: Tyco: Towards typicality-based collaborative filtering recommendation. In: ICTAI (2), pp. 97–104. IEEE Computer Society (2010)Google Scholar
  5. 5.
    Cai, Y., Li, Q., Xie, H., Yu, L.: Personalized Resource Search by Tag-Based User Profile and Resource Profile. In: Chen, L., Triantafillou, P., Suel, T. (eds.) WISE 2010. LNCS, vol. 6488, pp. 510–523. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Herlocker, J., Konstan, J., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd ACM SIGIR Conference, August 15-19, pp. 230–237 (1999)Google Scholar
  7. 7.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42, 30–37 (2009)CrossRefGoogle Scholar
  8. 8.
    Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations. In: Proceedings of the Eighteenth National Conference on Artificial Intelligence, pp. 187–192 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hao Han
    • 1
  • Yi Cai
    • 1
  • Yifeng Shao
    • 1
  • Qing Li
    • 2
  1. 1.School of Software EngineeringSouth China University of TechnologyGuangzhouChina
  2. 2.Department of Computer ScienceCity University of HongkongHongkongChina

Personalised recommendations