A Combined Collaborative Filtering Model for Social Influence Prediction in Event-Based Social Networks

  • Xiao Li
  • Xiang Cheng
  • Sen SuEmail author
  • Shuchen Li
  • Jianyu Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9645)


Event-based social networks (EBSNs) provide convenient online platforms for users to organize, attend and share social events. Understanding users’ social influences in social networks can benefit many applications, such as social recommendation and social marketing. In this paper, we focus on the problem of predicting users’ social influences on upcoming events in EBSNs. We formulate this prediction problem as the estimation of unobserved entries of the constructed user-event social influence matrix, where each entry represents the influence value of a user on an event. In particular, we define a user’s social influence on a given event as the proportion of the user’s friends who are influenced by him/her to attend the event. To solve this problem, we present a combined collaborative filtering model, namely, Matrix Factorization with Event Neighborhood (MF-EN) model, by incorporating event-based neighborhood method into matrix factorization. Due to the fact that the constructed social influence matrix is very sparse and the overlap values in the matrix are few, it is challenging to find reliable similar event neighbors using the widely adopted similarity measures (e.g., Pearson correlation and Cosine similarity). To address this challenge, we propose an additional information based neighborhood discovery (AID) method by considering three event-specific features in EBSNs. The parameters of our MF-EN model are determined by minimizing the associated regularized squared error function through stochastic gradient descent. We conduct a comprehensive performance evaluation on real-world datasets collected from DoubanEvent. Experimental results demonstrate the superiority of the proposed model compared to several alternatives.


Root Mean Square Error Social Influence Matrix Factorization Latent Dirichlet Allocation Event Neighborhood 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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


  1. 1.
    Agarwal, N., Liu, H., Tang, L., Yu, P.S.: Identifying the influential bloggers in a community. In: WSDM, pp. 207–218 (2008)Google Scholar
  2. 2.
    Anagnostopoulos, A., Kumar, R., Mahdian, M.: Influence and correlation in social networks. In: SIGKDD, pp. 7–15 (2008)Google Scholar
  3. 3.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  4. 4.
    Cai, Y., Lau, R.Y., Liao, S.S., Li, C., Leung, H.F., Ma, L.C.: Object typicality for effective web of things recommendations. Decis. Support Syst. 63, 52–63 (2014)CrossRefGoogle Scholar
  5. 5.
    Cai, Y., Leung, H.F., 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
  6. 6.
    Chin, A., Tian, J., Han, J., Niu, J.: A study of offline events and its influence on online social connections in douban. In: GreenCom and iThings/CPSCom, pp. 1021–1028 (2013)Google Scholar
  7. 7.
    Cui, P., Wang, F., Liu, S., Ou, M., Yang, S., Sun, L.: Who should share what?: item-level social influence prediction for users and posts ranking. In: SIGIR, pp. 185–194 (2011)Google Scholar
  8. 8.
    Du, R., Yu, Z., Mei, T., Wang, Z., Wang, Z., Guo, B.: Predicting activity attendance in event-based social networks: content, context and social influence. In: Ubicomp, pp. 425–434 (2014)Google Scholar
  9. 9.
    Embar, V.R., Bhattacharya, I., Pandit, V., Vaculín, R.: Online topic-based social influence analysis for the wimbledon championships. In: KDD, pp. 1759–1768 (2015)Google Scholar
  10. 10.
    Goyal, A., Bonchi, F., Lakshmanan, L.V.: Learning influence probabilities in social networks. In: WSDM, pp. 241–250 (2010)Google Scholar
  11. 11.
    Heinrich, G.: Parameter estimation for text analysis. Technical report (2005)Google Scholar
  12. 12.
    Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: SIGKDD, pp. 137–146 (2003)Google Scholar
  13. 13.
    Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: KDD, pp. 426–434 (2008)Google Scholar
  14. 14.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 8, 30–37 (2009)CrossRefGoogle Scholar
  15. 15.
    Lin, J.: Divergence measures based on the shannon entropy. IEEE Trans. Inf. Theory 37(1), 145–151 (1991)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Liu, B., Xiong, H.: Point-of-interest recommendation in location based social networks with topic and location awareness. In: SDM, vol. 13, pp. 396–404 (2013)Google Scholar
  17. 17.
    Liu, L., Tang, J., Han, J., Jiang, M., Yang, S.: Mining topic-level influence in heterogeneous networks. In: CIKM, pp. 199–208 (2010)Google Scholar
  18. 18.
    Liu, X., He, Q., Tian, Y., Lee, W.C., McPherson, J., Han, J.: Event-based social networks: linking the online and offline social worlds. In: KDD, pp. 1032–1040 (2012)Google Scholar
  19. 19.
    Qiao, Z., Zhang, P., Cao, Y., Zhou, C., Guo, L., Fang, B.: Combining heterogenous social and geographical information for event recommendation. In: AAAI, pp. 145–151 (2014)Google Scholar
  20. 20.
    Singla, P., Richardson, M.: Yes, there is a correlation: -from social networks to personal behavior on the web. In: WWW, pp. 655–664 (2008)Google Scholar
  21. 21.
    Strogatz, S.H.: Exploring complex networks. Nature 410(6825), 268–276 (2001)CrossRefGoogle Scholar
  22. 22.
    Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 4 (2009)CrossRefGoogle Scholar
  23. 23.
    Tang, J., Sun, J., Wang, C., Yang, Z.: Social influence analysis in large-scale networks. In: SIGKDD, pp. 807–816 (2009)Google Scholar
  24. 24.
    Wen, Y.T., Lei, P.R., Peng, W.C., Zhou, X.F.: Exploring social influence on location-based social networks. In: ICDM, pp. 1043–1048 (2014)Google Scholar
  25. 25.
    Weng, J., Lim, E.P., Jiang, J., He, Q.: Twitterrank: finding topic-sensitive influential twitterers. In: WSDM, pp. 261–270 (2010)Google Scholar
  26. 26.
    Xu, T., Zhong, H., Zhu, H., Xiong, H., Chen, E., Liu, G.: Exploring the impact of dynamic mutual influence on social event participation. In: SDM, pp. 262–270 (2015)Google Scholar
  27. 27.
    Ye, M., Liu, X., Lee, W.C.: Exploring social influence for recommendation: a generative model approach. In: SIGIR, pp. 671–680 (2012)Google Scholar
  28. 28.
    Yu, Z., Du, R., Guo, B., Xu, H., Gu, T., Wang, Z., Zhang, D.: Who should i invite for my party?: combining user preference and influence maximization for social events. In: Ubicomp, pp. 879–883(2015)Google Scholar
  29. 29.
    Zhang, C., Shou, L., Chen, K., Chen, G., Bei, Y.: Evaluating geo-social influence in location-based social networks. In: CIKM, pp. 1442–1451 (2012)Google Scholar
  30. 30.
    Zhang, J., Wang, C., Wang, J., Yu, J.X.: Inferring continuous dynamic social influence and personal preference for temporal behavior prediction. PVLDB 8(3), 269–280 (2014)MathSciNetGoogle Scholar
  31. 31.
    Zhang, W., Wang, J.: A collective bayesian poisson factorization model for cold-start local event recommendation. In: KDD, pp. 1455–1464 (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Xiao Li
    • 1
  • Xiang Cheng
    • 1
  • Sen Su
    • 1
    Email author
  • Shuchen Li
    • 1
  • Jianyu Yang
    • 1
  1. 1.State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijingChina

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