ABSTRACT
The newly emerging event-based social networks (EBSNs) extend social interaction from online to offline, providing an appealing platform for people to organize and participate realworld social events. In this paper, we investigate how to select potential participants in EBSNs from an event host's point of view. We formulate the problem as mining influential and preferable invitee set, considering from two complementary aspects. The first aspect concerns users' preference with respect to the event. The second aspect is influence maximization, which aims to influence the largest number of users to participate the event. In particular, we propose a novel Credit Distribution-User Influence Preference (CD-UIP) algorithm to find the most influential and preferable followers as the invitees. We collect a real-world dataset from a popular EBSNs called "Douban Events", and the experimental results on the dataset demonstrate the proposed algorithm outperforms the state-of-the-art prediction methods.
- X. Liu, Q. He, Y. Tian, W.-C. Lee, J. McPherson, and J. Han, "Event-based social networks: linking the online and offline social worlds," in Knowledge Discovery and Data Mining, 2012, pp. 1032--1040. Google ScholarDigital Library
- J. Han, J. Niu, A. Chin, W. Wang, C. Tong, and X. Wang, "How online social network affects offline events: A case study on douban," in Ubiquitous Intelligence & Computing and International Conference on Autonomic & Trusted Computing, 2012, pp. 752--757. Google ScholarDigital Library
- B. Xu, A. Chin, and D. Cosley, "On how event size and interactivity affect social networks," in CHI Extended Abstracts on Human Factors in Computing Systems, 2013, pp. 865--870. Google ScholarDigital Library
- E. Minkov, B. Charrow, J. Ledlie, S. J. Teller, and T. Jaakkola, "Collaborative future event recommendation," in International Conference on Information and Knowledge Management, 2010, pp. 819--828. Google ScholarDigital Library
- R. Du, Z. Yu, T. Mei, Z. Wang, Z. Wang, and B. Guo, "Predicting activity attendance in event-based social networks: content, context and social influence," in Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 2014, pp. 425--434. Google ScholarDigital Library
- D. MacLean, S. Hangal, S. K. Teh, M. S. Lam, and J. Heer, "Groups without tears: mining social topologies from email," in Proceedings of the 16th international conference on Intelligent user interfaces. ACM, 2011, pp. 83--92. Google ScholarDigital Library
- E. G. Boix, A. L. Carreton, C. Scholliers, T. Van Cutsem, W. De Meuter, and T. D'Hondt, "Flocks: enabling dynamic group interactions in mobile social networking applications," in Proceedings of the 2011 ACM Symposium on Applied Computing. ACM, 2011, pp. 425--432. Google ScholarDigital Library
- B. Guo, Z. Yu, D. Zhang, H. He, J. Tian, and X. Zhou, "Toward a group-aware smartphone sensing system," Pervasive Computing, IEEE, vol. 13, no. 4, pp. 80--88, 2014.Google ScholarCross Ref
- A. Goyal, F. Bonchi, and L. V. Lakshmanan, "A data-based approach to social influence maximization," Proceedings of the VLDB Endowment, vol. 5, no. 1, pp. 73--84, 2011. Google ScholarDigital Library
- D. Kempe, J. Kleinberg, and É. Tardos, "Maximizing the spread of influence through a social network," in Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2003, pp. 137--146. Google ScholarDigital Library
- A. Goyal, F. Bonchi, and L. V. Lakshmanan, "Learning influence probabilities in social networks," in Proceedings of the third ACM international conference on Web search and data mining. ACM, 2010, pp. 241--250. Google ScholarDigital Library
- M. Richardson and P. Domingos, "Mining knowledge-sharing sites for viral marketing," in Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2002, pp. 61--70. Google ScholarDigital Library
Index Terms
- Who should I invite for my party?: combining user preference and influence maximization for social events
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