Advertisement

Finding Similar Users over Multiple Attributes on the Basis of Intuitionistic Fuzzy Set

  • Haitao Wu
  • Shi Ying
Article
  • 47 Downloads

Abstract

Finding similar users is vital for many applications, such as collaborative filtering and recommendation systems. Unlike existing work that can only discover similar users according to one attribute, we propose a method based on intuitionistic fuzzy set for finding similar users according to multiple attributes: interest, behavior, and personal information. For a single attribute, such as user interest, the interest of two users may be partly similar, different in another aspect, and fuzzy in the remaining part. The three parts correspond exactly to the three concepts of the intuitionistic fuzzy numbers: membership, non-membership, and uncertainty degrees. And the integrated operator of the intuitionistic fuzzy set is just used for considering all attributes comprehensively. Based on this idea, we firstly introduce the method for determining the intuitionistic fuzzy numbers of each attribute. And then we use intuitionistic fuzzy hybrid average operators to integrate them to quantify the user similarity level. And we can further identify the similar users in terms of multiple attributes based on the level. The experiments based on Twitter data show our method outperforms the baselines.

Keywords

Intuitionistic fuzzy set Online social network Similar users 

Notes

Acknowledgments

The work is Supported by the National Natural Science Foundation of China under Grant No. 61373038.

References

  1. 1.
    Das AS, Datar M, Garg A, Rajaram S. (2007) Google news personalization: scalable online collaborative filtering. Proceedings of the 16th international conference on World Wide Web, 271–280Google Scholar
  2. 2.
    Zheng VW, Cao B, Zheng Y, Xie X, Yang Q. Collaborative filtering meets mobile recommendation: A user-centered approach. Year: 236–241Google Scholar
  3. 3.
    Wu X, Yan J, Liu N, Yan S, Chen Y, Chen Z. 2009) Probabilistic latent semantic user segmentation for behavioral targeted advertising. Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising, 10–17Google Scholar
  4. 4.
    Zhou YK, Mobasher B. (2006) Web user segmentation based on a mixture of factor analyzers. Proceedings of the 7th international conference on E-Commerce and Web Technologies, 11–20Google Scholar
  5. 5.
    Wang X, Liu H, Fan W. (2011) Connecting users with similar interests via tag network inference. ACM international conference on Information and knowledge management (CIKM), 1019–1024Google Scholar
  6. 6.
    Zhang L, Zhang B, Zheng J, Weng X, Wang M, Mei K. (2012) Discovering Similar User Models Based on Interest Tree. Proceedings of the 2012 I.E. 12th International Conference on Computer and Information Technology. 1046–1050Google Scholar
  7. 7.
    Li QZ, Wu YFB (2008) People search: Searching people sharing similar interests from the Web. Journal of the American Society for Information Science and Technology 59(1):111–125CrossRefGoogle Scholar
  8. 8.
    Slaninova K, Dolak R, Miskus M, Martinovic J, Snasel V. (2010) User Segmentation Based on Finding Communities with Similar Behavior on the Web Site. Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03, 75–78Google Scholar
  9. 9.
    Ma H, Cao H, Yang Q, Chen E, Tian J. (2012) A habit mining approach for discovering similar mobile users. Proceedings of the international conference on World Wide Web (WWW), 231–240Google Scholar
  10. 10.
    Xiao X, Zheng Y, Luo Q, Xie X. (2010) Finding similar users using category-based location history. SIGSPATIAL International Conference on Advances in Geographic Information Systems, 442–445Google Scholar
  11. 11.
    Zadeh LA (1965) Fuzzy sets. Information and Control 8(3):338–353MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Atanassov K (1986) Intuitionistic fuzzy sets. Fuzzy Sets and Systems 20(1):87–96MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Xu Z (2007) Intuitionistic fuzzy aggregation operators. IEEE Transactions on Fuzzy Systems 15(6):1179–1187CrossRefGoogle Scholar
  14. 14.
    Chen S-M, Tan J-M (1994) Handling multicriteria fuzzy decision-making problems based on vague set theory. Fuzzy Sets and Systems 67(2):163–172MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Hong DH, Choi C-H (2000) Multicriteria fuzzy decision-making problems based on vague set theory. Fuzzy Sets and Systems 114(1):103–113CrossRefzbMATHGoogle Scholar
  16. 16.
    Nisgav A, Patt-Shamir B (2011) Finding similar users in social networks. Theor. Comp. Sys. 49(4):720–737MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. The Journal of Machine Learning Research 3(4):993–1022zbMATHGoogle Scholar
  18. 18.
    Xiao C, Xue Y, Luo X, Wu Y (2013) Finding users with similar interests in online social networks. Journal of Computational Information Systems 9(23):9523–9531Google Scholar
  19. 19.
    Takemura H, Tajima K (2012) Tweet classification based on their lifetime duration. Proceedings of the 21st ACM international conference on Information and knowledge management. 2367–2370Google Scholar
  20. 20.
    Jarvelin K, Kek J (2000) inen. IR evaluation methods for retrieving highly relevant documents. Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, 41–48Google Scholar
  21. 21.
    Manning CD, Raghavan P, Schutze H. (2008) Introduction to Information Retrieval. Cambridge University PressGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Software Engineering, Computer SchoolWuhan UniversityWuhanChina
  2. 2.Software SchoolHuanghuai UniversityZhumadianChina

Personalised recommendations