Social Network-Based Event Recommendation

  • Dinh Tuyen Hoang
  • Van Cuong Tran
  • Dosam Hwang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10448)


The number of events generated on social networks has been growing quickly in recent years. It is difficult for users to find events that most suitably match their favorites. As a solution, the recommender system appears to solve this problem. However, event recommendation is significantly different from traditional recommendations, such as products and movies. Social events are created continuously, and only valid for a short time, so recommending a past event is meaningless. In this paper, we proposed a new even recommendation method based on social networks. First, the behavior of users be detected in order to build the user’s profile. Then the users’ relationship is extracted to measure the interaction strength between them. That is a fundamental factor affecting a decision of a user to attend events. In addition, the opinions about attended events are taken into account to evaluate the satisfaction of attendees by using deep learning method. Twitter is used as a case study for the method. The experiment shows that the method achieves promising results in comparison to other methods.


Recommender system Event recommedation Social event 



This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (21C000151).


  1. 1.
    Kang, D., Han, D., Park, N., Kim, S., Kang, U., Lee, S.: Eventera: real-time event recommendation system from massive heterogeneous online media. In: 2014 IEEE International Conference on Data Mining Workshop, pp. 1211–1214. IEEE (2014)Google Scholar
  2. 2.
    Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents. arXiv preprint arXiv:1405.4053 (2014)
  3. 3.
    Li, R., Lei, K.H., Khadiwala, R., Chang, K.C.C.: TEDAS: a twitter-based event detection and analysis system. In: 2012 IEEE 28th International Conference on Data Engineering, pp. 1273–1276. IEEE (2012)Google Scholar
  4. 4.
    Magnuson, A., Dialani, V., Mallela, D.: Event recommendation using twitter activity. In: Proceedings of the 9th ACM Conference on Recommender Systems, pp. 331–332. ACM (2015)Google Scholar
  5. 5.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
  6. 6.
    Nguyen, N.T.: Advanced Methods for Inconsistent Knowledge Management. Advanced Information and Knowledge Processing. Springer, London (2008). doi: 10.1007/978-1-84628-889-0CrossRefzbMATHGoogle Scholar
  7. 7.
    Nguyen, V.D., Merayo, M.G.: Intelligent collective: some issues with collective cardinality. J. Inf. Telecommun. (2017). doi: 10.1080/24751839.2017.1323702CrossRefGoogle Scholar
  8. 8.
    Ozdikis, O., Senkul, P., Oguztuzun, H.: Semantic expansion of hashtags for enhanced event detection in twitter. In: Proceedings of the 1st International Workshop on Online Social Systems. Citeseer (2012)Google Scholar
  9. 9.
    Qiao12, Z., Zhang, P., Zhou, C., Cao, Y., Guo, L., Zhang, Y.: Event recommendation in event-based social networks. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence. AAAI (2014)Google Scholar
  10. 10.
    Sriharee, G.: An ontology-based approach to auto-tagging articles. Vietnam J. Comput. Sci. 2(2), 85–94 (2015)CrossRefGoogle Scholar
  11. 11.
    Uddin, M.N., Duong, T.H., Nguyen, N.T., Qi, X.M., Jo, G.S.: Semantic similarity measures for enhancing information retrieval in folksonomies. Expert Syst. Appl. 40(5), 1645–1653 (2013)CrossRefGoogle Scholar
  12. 12.
    Zhang, Y., Wu, H., Sorathia, V.S., Prasanna, V.K.: Event recommendation in social networks with linked data enablement. In: ICEIS, vol. 2, pp. 371–379 (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Dinh Tuyen Hoang
    • 1
  • Van Cuong Tran
    • 2
  • Dosam Hwang
    • 1
  1. 1.Department of Computer EngineeringYeungnam UniversityGyeongsanSouth Korea
  2. 2.Quang Binh UniversityQuang BinhViet Nam

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