Abstract
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.
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Notes
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The RSVP (“yes” or “maybe”) indicates that a user wants to attend or is interested in an event. We assume that a user will attend the events which he/she has expressed RSVP (“yes”) to.
References
Agarwal, N., Liu, H., Tang, L., Yu, P.S.: Identifying the influential bloggers in a community. In: WSDM, pp. 207–218 (2008)
Anagnostopoulos, A., Kumar, R., Mahdian, M.: Influence and correlation in social networks. In: SIGKDD, pp. 7–15 (2008)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
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)
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)
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)
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)
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)
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)
Goyal, A., Bonchi, F., Lakshmanan, L.V.: Learning influence probabilities in social networks. In: WSDM, pp. 241–250 (2010)
Heinrich, G.: Parameter estimation for text analysis. Technical report (2005)
Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: SIGKDD, pp. 137–146 (2003)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: KDD, pp. 426–434 (2008)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 8, 30–37 (2009)
Lin, J.: Divergence measures based on the shannon entropy. IEEE Trans. Inf. Theory 37(1), 145–151 (1991)
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)
Liu, L., Tang, J., Han, J., Jiang, M., Yang, S.: Mining topic-level influence in heterogeneous networks. In: CIKM, pp. 199–208 (2010)
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)
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)
Singla, P., Richardson, M.: Yes, there is a correlation: -from social networks to personal behavior on the web. In: WWW, pp. 655–664 (2008)
Strogatz, S.H.: Exploring complex networks. Nature 410(6825), 268–276 (2001)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 4 (2009)
Tang, J., Sun, J., Wang, C., Yang, Z.: Social influence analysis in large-scale networks. In: SIGKDD, pp. 807–816 (2009)
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)
Weng, J., Lim, E.P., Jiang, J., He, Q.: Twitterrank: finding topic-sensitive influential twitterers. In: WSDM, pp. 261–270 (2010)
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)
Ye, M., Liu, X., Lee, W.C.: Exploring social influence for recommendation: a generative model approach. In: SIGIR, pp. 671–680 (2012)
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)
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)
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)
Zhang, W., Wang, J.: A collective bayesian poisson factorization model for cold-start local event recommendation. In: KDD, pp. 1455–1464 (2015)
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The work was supported by National Natural Science Foundation of China under Grant 61502047.
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Li, X., Cheng, X., Su, S., Li, S., Yang, J. (2016). A Combined Collaborative Filtering Model for Social Influence Prediction in Event-Based Social Networks. In: Gao, H., Kim, J., Sakurai, Y. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9645. Springer, Cham. https://doi.org/10.1007/978-3-319-32055-7_13
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