Mining User Interests from Information Sharing Behaviors in Social Media

  • Tingting Wang
  • Hongyan Liu
  • Jun He
  • Xiaoyong Du
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7819)


Mining user interests and preference plays an important role for many applications such as information retrieval and recommender systems. This paper intends to study how to infer interests for new users and inactive users from social media. Although some recently proposed methods can mine user interests efficiently, these works cannot make full use of relationship between users in their social network. In this paper, we propose a novel approach to infer interests of new users or inactive users based on social connections between users. A random-walk based mutual reinforcement model combining both text and link information is developed in the approach. More importantly, we compare the contribution of different social connections such as “follow”, “retweet”, “mention”, and “comment” to interest sharing. Experiments conducted on real dataset show that our method is effective and outperforms existing algorithms, and different social connections have different impacts on mining user interests.


Interest inferring Social networks Information sharing behaviors 


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  1. 1.
    Li, X., Guo, L., Zhao, Y.E.: Tag-based social interest discovery. In: Proc. of the 17th International Conference on World Wide Web, pp. 675–684. ACM, New York (2008)CrossRefGoogle Scholar
  2. 2.
    Agichtein, E., Brill, E., Dumais, S.T.: Improving web search ranking by incorporating user behavior information. In: Proc. of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 19–26. ACM, New York (2006)CrossRefGoogle Scholar
  3. 3.
    Glodberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Communications of the ACM-Special Issue on Information Filtering 35(12), 61–70 (1992)Google Scholar
  4. 4.
    Wen, Z., Lin, C.-Y.: On the quality of inferring interests from social neighbors. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2010, pp. 373–382. ACM, New York (2010)CrossRefGoogle Scholar
  5. 5.
    Blei, D.M., Nq, A.Y., Jordan, M.I.: Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)zbMATHGoogle Scholar
  6. 6.
    Claypool, M., Brown, D., Le, P., Waseda, M.: Inferring user Interest. IEEE Internet Computing, 32–39 (2001)Google Scholar
  7. 7.
    Qiu, F., Cho, J.: Automatic identification of user interest for personalized search. In: Proceedings of the 15th International Conference on World Wide Web, WWW 2006, pp. 727–736. ACM, New York (2006)CrossRefGoogle Scholar
  8. 8.
    Kim, H.R., Chan, P.K.: Learning implicit user interest hierarchy for context in personalization. In: Proceedings of the 8th International Conference on Intelligent User Interfaces, IUI 2003, pp. 101–108. ACM, New York (2003)Google Scholar
  9. 9.
    Shepitsen, A., Gemmell, J., Mobasher, B., Burke, R.D.: Personalized recommendation in social tagging systems using hierarchical clustering. In: Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys 2008, pp. 259–266. ACM, New York (2008)CrossRefGoogle Scholar
  10. 10.
    Stoyanovich, J., Amer-Yahia, S., Markow, C., Yu, C.: Leveraging tagging to model user interests in In: AAAI Spring Symposium on Social Information Processing (2008)Google Scholar
  11. 11.
    White, R.W., Bailey, P., Chen, L.: Predicting user interests from contextual information. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009, pp. 363–370. ACM, New York (2009)CrossRefGoogle Scholar
  12. 12.
    Steyvers, M., Griffiths, T.: Probabilistic topic models. In: Landauer, T., McNamara, D., Dennis, S., Kintsch, W. (eds.) Latent Semantic Analysis: A Road to Meaning. Lawrence Erlbaum (2007)Google Scholar
  13. 13.
    Weng, J., Lim, E.-P., Jiang, J., He, Q.: TwitterRank: Finding topic-sensitive influential twitterers. In: Proceedings of the Third International ACM Conference on Web Search and Data Mining, pp. 261–270 (2010)Google Scholar
  14. 14.
  15. 15.
    Milstein, S., Chowdhury, A., Hochmuth, G., Lorica, B., Magoulas, R.: Twitter and the micro-messaging revolution: Communication, connections, and immediacy-140 characters at a time. O’Reilly Report (November 2008)Google Scholar
  16. 16.
  17. 17.
    McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: Homophily in social networks. Annual Review of Sociology 27(1), 415–444 (2001)CrossRefGoogle Scholar
  18. 18.
    Tong, H., Faloutsos, C., Pan, J.-Y.: Fast random walk with restart and its applications. In: Proceeding of the 6th international Conference on Data Mining, pp. 613–622.Google Scholar
  19. 19.
    Adamic, L.A., Adar, E.: Friends and Neighbors on the Web. Social Networks 25(3), 211–230 (2003)CrossRefGoogle Scholar
  20. 20.
    Welch, M.J., Schonfeld, U., He, D., Cho, J.: Topic semantics of twitter links. In: Proceedings of the Fourth International ACM Conference on Web Search and Data Mining (WSDM 2011), Hong Kong, China (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tingting Wang
    • 1
    • 2
  • Hongyan Liu
    • 3
  • Jun He
    • 1
    • 2
  • Xiaoyong Du
    • 1
    • 2
  1. 1.Key Labs of Data Engineering and Knowledge EngineeringMinistry of EducationChina
  2. 2.School of InformationRenmin University of ChinaChina
  3. 3.Department of Management Science and EngineeringTsinghua UniversityChina

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