Advertisement

Followee Recommendation in Event-Based Social Networks

  • Shuchen Li
  • Xiang ChengEmail author
  • Sen Su
  • Le Jiang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9645)

Abstract

Recent years have witnessed the rapid growth of event-based social networks (EBSNs) such as Plancast and DoubanEvent. In these EBSNs, followee recommendation which recommends new users to follow can bring great benefits to both users and service providers. In this paper, we focus on the problem of followee recommendation in EBSNs. However, the sparsity and imbalance of the social relations in EBSNs make this problem very challenging. Therefore, by exploiting the heterogeneous nature of EBSNs, we propose a new method called Heterogenous Network based Followee Recommendation (HNFR) for our problem. In the HNFR method, to relieve the problem of data sparsity, we combine the explicit and latent features captured from both the online social network and the offline event participation network of an EBSN. Moreover, to overcome the problem of data imbalance, we propose a Bayesian optimization framework which adopts pairwise user preference on both the social relations and the events, and aims to optimize the area under ROC curve (AUC). The experiments on real-world data demonstrate the effectiveness of our method.

Keywords

Followee recommendation Event-based social networks Heterogenous network 

Notes

Acknowledgements

The work was supported by National Natural Science Foundation of China under Grant 61502047.

References

  1. 1.
    Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25(3), 211–230 (2003)CrossRefGoogle Scholar
  2. 2.
    Barbieri, N., Bonchi, F., Manco, G.: Who to follow and why: link prediction with explanations. In: SIGKDD, pp. 1266–1275 (2014)Google Scholar
  3. 3.
    Cai, Y., Lau, R.Y.K., Liao, S.S.Y., Li, C., Leung, H., Ma, L.C.K.: Object typicality for effective web of things recommendations. Decis. Support Syst. 63, 52–63 (2014)CrossRefGoogle Scholar
  4. 4.
    Cai, Y., Leung, H., Li, Q., Min, H., Tang, J., Li, J.: Typicality-based collaborative filtering recommendation. IEEE Trans. Knowl. Data Eng. 26(3), 766–779 (2014)CrossRefGoogle Scholar
  5. 5.
    Chen, J., Geyer, W., Dugan, C., Muller, M.J., Guy, I.: Make new friends, but keep the old: recommending people on social networking sites. In: CHI, pp. 201–210 (2009)Google Scholar
  6. 6.
    Guo, G., Zhang, J., Sun, Z., Yorke-Smith, N.: Librec: a java library for recommender systems. In: Posters, Demos, Late-breaking Results and Workshop Proceedings of User Modeling, Adaptation, and Personalization (UMAP 2015) (2015)Google Scholar
  7. 7.
    Guy, I., Ronen, I., Wilcox, E.: Do you know?: recommending people to invite into your social network. In: IUI, pp. 77–86 (2009)Google Scholar
  8. 8.
    Hannon, J., Bennett, M., Smyth, B.: Recommending twitter users to follow using content and collaborative filtering approaches. In: RecSys, pp. 199–206 (2010)Google Scholar
  9. 9.
    Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: ICDM, pp. 263–272 (2008)Google Scholar
  10. 10.
    Koren, Y., Bell, R.M., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Comput. 42(8), 30–37 (2009)CrossRefGoogle Scholar
  11. 11.
    Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: NIPS, pp. 556–562 (2000)Google Scholar
  12. 12.
    Liben-Nowell, D., Kleinberg, J.M.: The link prediction problem for social networks. In: CIKM, pp. 556–559 (2003)Google Scholar
  13. 13.
    Liu, X., He, Q., Tian, Y., Lee, W., McPherson, J., Han, J.: Event-based social networks: linking the online and offline socialworlds. In: SIGKDD, pp. 1032–1040 (2012)Google Scholar
  14. 14.
    Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part II. LNCS, vol. 6912, pp. 437–452. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  15. 15.
    Pan, R., Zhou, Y., Cao, B., Liu, N.N., Lukose, R.M., Scholz, M., Yang, Q.: One-class collaborative filtering. In: ICDM, pp. 502–511 (2008)Google Scholar
  16. 16.
    Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: bayesian personalized ranking from implicit feedback. In: UAI, pp. 452–461 (2009)Google Scholar
  17. 17.
    Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: NIPS, pp. 1257–1264 (2007)Google Scholar
  18. 18.
    Scellato, S., Noulas, A., Mascolo, C.: Exploiting place features in link prediction on location-based social networks. In: SIGKDD, pp. 1046–1054 (2011)Google Scholar
  19. 19.
    Wan, S., Lan, Y., Guo, J., Fan, C., Cheng, X.: Informational friend recommendation in social media. In: SIGIR, pp. 1045–1048 (2013)Google Scholar
  20. 20.
    Yuan, G., Murukannaiah, P.K., Zhang, Z., Singh, M.P.: Exploiting sentiment homophily for link prediction. In: RecSys, pp. 17–24 (2014)Google Scholar
  21. 21.
    Zhao, G., Lee, M., Hsu, W., Chen, W., Hu, H.: Community-based user recommendation in uni-directional social networks. In: CIKM, pp. 189–198 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijingChina

Personalised recommendations