Advertisement

Recommend Algorithm Combined User-user Neighborhood Approach with Latent Factor Model

  • Xiaojiao YaoEmail author
  • Beihai Tan
  • Chao Hu
  • Weijun Li
  • Zhenhao Xu
  • Zipei Zhang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 691)

Abstract

The item-item neighborhood model became very volatile for current items rapidly replaced, such as online article and news items. And the neighborhood model faces the problem of data sparsity and cold start. The factor model can alleviate data sparseness problem, but it does not take the historical behavior data into consideration. Therefore, in this paper, we proposes a recommendation algorithm based on user-user neighborhood model and latent factor model, which can make accuracy improved significantly and can effectively address the problem of data sparsity. When the number of neighborhoods k increases, the accuracy of the algorithm has improved. The experiment result shows that this approach is correct and feasible.

Keywords

Recommend system User-user model Latent factor model 

Notes

Acknowledgment

This work was supported by the National Natural Science Foundation of China (Grant No. 61203117) and Yao is the corresponding author.

References

  1. 1.
    Glodberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35, 61–70 (1992)CrossRefGoogle Scholar
  2. 2.
    Yao, P., Zou, D., Niu, B.: Collaborative filtering recommender algorithm based on user preferences and project properties Beijing. Comput. Syst. Appl. 24(7), 15–21 (2015)Google Scholar
  3. 3.
    Ketchantany, W., Derrde, S., Martin, L.: Pearson-based mixture model for color object tracking. Mach. Vis. Appl. 19, 457–466 (2008)CrossRefGoogle Scholar
  4. 4.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering, pp. 43–52. Morgan Kaufmann Publishers, Wisconsin (1998)Google Scholar
  5. 5.
    Linden, G., Smith, B., York, J.: Amazon.com recommendations item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)CrossRefGoogle Scholar
  6. 6.
    Bell, R., Koren, Y., Volinsky, C.: Modeling relationships at multiple scales to improve accuracy of large recommender systems. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2007), New York, pp. 95–104 (2007)Google Scholar
  7. 7.
    Ricci, F., Rokach, L., Shapira, B.: Recommender System Handbooks, pp. 83–84. Springer, New York (2011)CrossRefGoogle Scholar
  8. 8.
    Xiang, L.: Recommended System Practice, pp. 184–193. Posts and Telecom Press, Beijing (2012)Google Scholar
  9. 9.
  10. 10.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Comput. 42(8), 30–37 (2009)CrossRefGoogle Scholar
  11. 11.
    Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of ACM SIGKDD Conference, pp. 426–434 (2008)Google Scholar
  12. 12.
    Sarwar, B., Karypis, G., Konstan, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295. ACM (2001)Google Scholar
  13. 13.
    Koren, Y.: Collaborative filtering with temporal dynamics. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 447–456 (2009)Google Scholar
  14. 14.

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Xiaojiao Yao
    • 1
    Email author
  • Beihai Tan
    • 1
  • Chao Hu
    • 1
  • Weijun Li
    • 1
  • Zhenhao Xu
    • 1
  • Zipei Zhang
    • 1
  1. 1.Guangdong University of TechnologyGuangzhouChina

Personalised recommendations