An Optimization of Collaborative Filtering Personalized Recommendation Algorithm Based on Time Context Information

  • Xian Jin
  • Qin Zheng
  • Lily Sun
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 449)


This paper proposes an improved collaborative filtering algorithm based on time context information. Introducing the time information into the traditional collaborative filtering algorithm, the essay studies the changes of user preference in the time dimension. In this paper the time information includes three aspects: the time context information; the interest decays with the time; items similarity factor. This paper first uses Pearson correlation coefficient calculates time context similarity, pre-filtering the time-context. Through the experiment, the improved algorithm has higher accuracy than the traditional filter algorithms without time factor in the TOP-N recommendation list. It proves that time-context information of user’s can affect the user’s preference.


Personalized recommendation collaborative filtering time-context 


  1. 1.
    Abbar, S., Bouzeghoub, M., Lopez, S.: Context-Aware recommender systems: A service-oriented approach. In: Proceedings of the VLDB Workshop on PersDBLyon (2009)Google Scholar
  2. 2.
    Agrawal, R., Rantzau, R., Terzi, E.: Context-sensitive ranking. In: Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data, pp. 383–394 (2006)Google Scholar
  3. 3.
    Billsus, D., Pazzani, M.J.: Learning collaborative information filters. In: Proceedings of the 15th National Conference on Artificial Intelligence, vol. 4(20), pp. 235–259. AAAI Press, Menlo Park (1998)Google Scholar
  4. 4.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, vol. 461, pp. 143–162 (2008)Google Scholar
  5. 5.
    Chien, Y.H., George, E.I.: A Bayesian model for collaborative filtering. In: Proceedings of the 7th Int. Workshop on Artificial Intelligence and Statistics, vol. 6(1), pp. 152–159 (1999)Google Scholar
  6. 6.
    Dey, A.K.: Understanding and using context. Personal and Ubiquitous Computing 8(10), 256–268 (2001)Google Scholar
  7. 7.
    Getoor, L., Sahami, M.: Using probabilistic relational models for collaborative fi1tering. In: Proceedings of the Workshop Web Usage Analysis and User Profiling, vol. 9(12), pp. 134–147 (1999)Google Scholar
  8. 8.
    Herlocker, J.L., Konstan, J.: Content-independent task-focused recommendation. IEEE Internet Computing, 240–257 (2001)Google Scholar
  9. 9.
    Kim, J.H., Jung, K.Y., Ryu, J.K.: Design of Ubiquitous Music Recommendation System. In: Proceedings of the 4th International Conference on Networked Computing and Advanced Information Management, pp. 250–263 (2008)Google Scholar
  10. 10.
    Jani, J.: Collaboration in Context-Aware Mobile Phone Applications. Proceedings of the 38th Hawaii International Conference on System Science 5(13), 156–172 (2005)Google Scholar
  11. 11.
    Oku, K., Nakajima, S., Miyazak, J.: Context-aware SVM for context-dependent information recommendation. In: Proceedings of the 7th International Conference on Mobile Data Management, vol. 5(19), pp. 189–207 (2006)Google Scholar
  12. 12.
    Kobsa, A.: Generic user modeling system: user modeling and user-adapted interaction. Information Research System 9(12), 143–165 (2001)Google Scholar
  13. 13.
    Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations. In: Proceedings of the l8th Conference on Artificial Intelligence, vol. 5(15), pp. 243–264 (2002)Google Scholar
  14. 14.
    Park, H.S., Yoo, J.O., Cho, S.B.: A context-aware music recommendation system using fuzzy Bayesian networks with utility theory. In: Proceedings of IEEE Fuzzy Systems and Knowledge Discovery (2006)Google Scholar
  15. 15.
    Pavlov, D., Pennock, D.: A maximum entropy approach to collaborative filtering in dynamic, sparse and high dimensional domains. In: Proceedings of the 16th Annual Conference on Neural Information Processing Systems (2002)Google Scholar
  16. 16.
    Pazzani, M., Billsus, D.: Learning and revising user profiles: the identification of interesting web sites. Machine Learning (1997)Google Scholar
  17. 17.
    Stender, M., Ritz, T.: Modeling of B2B mobile commerce processes. International Journal of Production Economics 9(19), 359–372 (2006)Google Scholar
  18. 18.
    Hong, Y., Yun, L.Z.: Collaborative filtering recommendation algorithm based on forgetting curve. Journal of Nanjing University 46(5), 34–38 (2010)Google Scholar
  19. 19.
    Chunxiao, X., Fengrong, G., Zhan, S.N.: A collaborative filtering recommendation algorithm to adapt to the user interest change. Journal of Computer Research and Development 44(2), 10–14 (2007)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  • Xian Jin
    • 1
  • Qin Zheng
    • 1
    • 2
  • Lily Sun
    • 3
  1. 1.School of Information Management and EngineeringShanghai University of Finance, and EconomicsShanghaiChina
  2. 2.South University of Science and Technology of ChinaShenzhenChina
  3. 3.School of Systems EngineeringUniversity of ReadingReadingUK

Personalised recommendations