Predicting activity attendance in event-based social networks: content, context and social influence

ABSTRACT

The newly emerging event-based social networks (EBSNs) connect online and offline social interactions, offering a great opportunity to understand behaviors in the cyber-physical space. While existing efforts have mainly focused on investigating user behaviors in traditional social network services (SNS), this paper aims to exploit individual behaviors in EBSNs, which remains an unsolved problem. In particular, our method predicts activity attendance by discovering a set of factors that connect the physical and cyber spaces and influence individual's attendance of activities in EBSNs. These factors, including content preference, context (spatial and temporal) and social influence, are extracted using different models and techniques. We further propose a novel Singular Value Decomposition with Multi-Factor Neighborhood (SVD-MFN) algorithm to predict activity attendance by integrating the discovered heterogeneous factors into a single framework, in which these factors are fused through a neighborhood set. Experiments based on real-world data from Douban Events demonstrate that the proposed SVD-MFN algorithm outperforms the state-of-the-art prediction methods.

References

1. L. Backstrom, D. P. Huttenlocher, J. M. Kleinberg, and X. Lan. Group formation in large social networks: membership, growth, and evolution. In Knowledge Discovery and Data Mining, pages 44--54, 2006.
2. A. Ben-David. Comparison of classification accuracy using cohens weighted kappa. Expert Systems with Applications, 34(2):825--832, 2008.
3. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. Advances in neural information processing systems, 1:601--608, 2002.
4. D. Bollegala, Y. Matsuo, and M. Ishizuka. Measuring semantic similarity between words using web search engines. In World Wide Web Conference Series, pages 757--766, 2007.
5. Y. Cheng, G. Qiu, J. Bu, K. Liu, Y. Han, C. Wang, and C. Chen. Model bloggers' interests based on forgetting mechanism. In World Wide Web Conference Series, pages 1129--1130, 2008.
6. E. Cho, S. A. Myers, and J. Leskovec. Friendship and mobility: user movement in location-based social networks. In Knowledge Discovery and Data Mining, pages 1082--1090. ACM, 2011.
7. C. Cornelis, X. Guo, J. Lu, and G. Zhang. A Fuzzy Relational Approach to Event Recommendation. In Indian International Conference on Artificial Intelligence, pages 2231--2242, 2005.
8. D. J. Crandall, L. Backstrom, D. Cosley, S. Suri, D. Huttenlocher, and J. Kleinberg. Inferring social ties from geographic coincidences. Proceedings of the National Academy of Sciences, 107(52):22436--22441, 2010.
9. E. M. Daly and W. Geyer. Effective event discovery: using location and social information for scoping event recommendations. In Proceedings of ACM Conference on Recommender Systems, pages 277--280. ACM, 2011.
10. T. L. Griffiths. Finding scientific topics. Proceedings of The National Academy of Sciences, 101:5228--5235, 2004.
11. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The WEKA data mining software: an update. Sigkdd Explorations, 11:10--18, 2009.
12. 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, pages 752--757, 2012.
13. J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. In Research and Development in Information Retrieval, pages 230--237, 1999.
14. R. Klamma, P. M. Cuong, and Y. Cao. You never walk alone: Recommending academic events based on social network analysis. In Complex Sciences, pages 657--670. 2009.
15. Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8):30--37, 2009.
16. D. H. Lee. PITTCULT: trust-based cultural event recommender. In Conference on Recommender Systems, pages 311--314, 2008.
17. Y. Li, D. Mclean, Z. A. Bandar, J. D. O'Shea, and K. A. Crockett. Sentence Similarity Based on Semantic Nets and Corpus Statistics. IEEE Transactions on Knowledge and Data Engineering, 18:1138--1150, 2006.
18. Y.-M. Li, C.-T. Wu, and C.-Y. Lai. A social recommender mechanism for e-commerce: Combining similarity, trust, and relationship. Decision Support Systems, 55(3):740--752, 2013.
19. J. Lin. Divergence measures based on the Shannon entropy. IEEE Transactions on Information Theory, 37:145--151, 1991.
20. 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, pages 1032--1040, 2012.
21. A. K. Menon, K.-P. Chitrapura, S. Garg, D. Agarwal, and N. Kota. Response prediction using collaborative filtering with hierarchies and side-information. In Knowledge Discovery and Data Mining, pages 1032--1040, 2012.
22. E. Minkov, B. Charrow, J. Ledlie, S. J. Teller, and T. Jaakkola. Collaborative future event recommendation. In International Conference on Information and Knowledge Management, pages 819--828, 2010.
23. A. Noulas, S. Scellato, C. Mascolo, and M. Pontil. An empirical study of geographic user activity patterns in foursquare. International Conference on Weblogs and Social Media, pages 70--573, 2011.
24. T. Peng, W. Zuo, and F. He. Svm based adaptive learning method for text classification from positive and unlabeled documents. Knowledge and Information Systems, 16(3):281--301, 2008.
25. J. Sang, T. Mei, J.-T. Sun, C. Xu, and S. Li. Probabilistic sequential pois recommendation via check-in data. In Proceedings of International Conference on Advances in Geographic Information Systems, pages 402--405, 2012.
26. Z. Wang, D. Zhang, X. Zhou, D. Yang, Z. Yu, and Z. Yu. Discovering and profiling overlapping communities in location-based social networks. Systems, Man, and Cybernetics: Systems, IEEE Transactions on, 44(4):499--509, April 2014.
27. 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, pages 865--870, 2013.
28. D. Yang, D. Zhang, Z. Yu, and Z. Yu. Fine-grained preference-aware location search leveraging crowdsourced digital footprints from lbsns. In Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing, pages 479--488, 2013.
29. M. Ye, X. Liu, and W.-C. Lee. Exploring social influence for recommendation: a generative model approach. In Proceedings of ACM International Conference on Research and Development in Information Retrieval, pages 671--680, 2012.
30. Z. Yu, Y. Yang, X. Zhou, Y. Zheng, and X. Xing. Investigating how user's activities in both virtual and physical world impact each other leveraging lbsn data. International Journal of Distributed Sensor Networks, 2014.
31. H. Zhuang, A. Chin, S. Wu, W. Wang, X. Wang, and J. Tang. Inferring geographic coincidence in ephemeral social networks. In Machine Learning and Knowledge Discovery in Databases, pages 613--628. 2012.
32. J. Zhuang, T. Mei, S. C. Hoi, Y.-Q. Xu, and S. Li. When recommendation meets mobile: contextual and personalized recommendation on the go. In Proceedings of the ACM International Conference on Ubiquitous Computing, pages 153--162, 2011.

Index Terms

• Information systems

  • World Wide Web

    • Web applications

    • Web services