Advertisement

A New Approach for Collaborative Filtering Based on Mining Frequent Itemsets

  • Phung Do
  • Vu Thanh Nguyen
  • Tran Nam Dung
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7803)

Abstract

As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first propose a new CF model-based approach which has been implemented by basing on mining frequent itemsets technique with the assumption that “The larger the support of an item is, the higher it’s likely that this item will occur in some frequent itemset, is”. We then present the enhanced techniques such as the followings: bits representations, bits matching as well bits mining in order to speeding-up the algorithm processing with CF method.

Keywords

Collaborative Filtering mining frequent itemsets bit matching bit mining 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Movielens dataset 2011. Home page is http://www.movielens.org, Download dataset from http://www.grouplens.org/node/12
  2. 2.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Elsevier Inc. (2006)Google Scholar
  3. 3.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems 22(1), 5–53 (2004)CrossRefGoogle Scholar
  4. 4.
    Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing 7(1), 76–80 (2003)CrossRefGoogle Scholar
  5. 5.
    Hofmann, T.: Latent semantic models for collaborative filtering. ACM Transactions on Information Systems 22(1), 89–115 (2004)CrossRefGoogle Scholar
  6. 6.
    Breese, J., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, UAI 1998 (1998)Google Scholar
  7. 7.
    Miyahara, K., Pazzani, M.J.: Collaborative filtering withthe simple Bayesian classifier. In: Proceedings of the 6th Pacific Rim International Conference on Artificial Intelligence, pp. 679–689 (2000)Google Scholar
  8. 8.
    Su, X., Khoshgoftaar, T.M.: Collaborative filtering for multi-class data using belief nets algorithms. In: Proceedings of the International Conference on Tools with Artificial Intelligence, ICTAI 2006, pp. 497–504 (2006)Google Scholar
  9. 9.
    Ungar, L.H., Foster, D.P.: Clustering methods for collaborative filtering. In: Proceedings of the Workshop on Recommendation Systems. AAAI Press (1998)Google Scholar
  10. 10.
    Chee, S.H.S., Han, J., Wang, K.: RecTree: an efficient collaborative filtering method. In: Proceedings of the 3rd International Conference on Data Warehousingand Knowledge Discovery, pp. 141–151 (2001)Google Scholar
  11. 11.
    Shani, G., Heckerman, D., Brafman, R.I.: An MDP-based recommender system. Journal of Machine Learning Research 6, 1265–1295 (2005)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Billsus, D., Pazzani, M.: Learning collaborative information filters. In: Proceedings of the 15th International Conference on Machine Learning, ICML 1998 (1998)Google Scholar
  13. 13.
    Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Analysis of recommendation algorithms for E-commerce. In: Proceedings of the ACM E-Commerce, Minneapolis, Minn, USA, pp. 158–167 (2000)Google Scholar
  14. 14.
    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, WWW 2001, pp. 285–295 (May 2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Phung Do
    • 1
  • Vu Thanh Nguyen
    • 1
  • Tran Nam Dung
    • 2
  1. 1.University of Information TechnologyHo Chi Minh CityVietnam
  2. 2.University of Natural ScienceHo Chi Minh CityVietnam

Personalised recommendations