I2Rec: An Iterative and Interactive Recommendation System for Event-Based Social Networks

  • Cailing Dong
  • Yilin Shen
  • Bin ZhouEmail author
  • Hongxia Jin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9708)


Event-based social networks (EBSNs) such as Meetup and Plancast have been emerging in recent years. In addition to the online virtual groups and connections on many existing Online Social Networks (OSNs), EBSNs also provide a platform for users to initialize and manage offline physical events in which user’s activities are strongly geographically constrained. As an increasing number of users are attracted by EBSNs, it is highly desirable to provide users with accurate recommendations of both online groups and offline events, which has become an urgent need. In this paper, we propose a comprehensive study for recommending users online groups and offline events on EBSNs. To represent user’s interactions via a timeline horizon, we design an I terative and I nteractive Rec ommendation System (I \(^2\) Rec), which couples both online user activities and offline social events together for providing more accurate recommendation. Our proposed \(I^2Rec\) system infers a user’s online and offline activities in turn and iteratively enriches the training information based on user’s feedback. Using the large-scale real-world dataset crawled from Meetup, our recommendation system outperforms other baseline approaches significantly. More importantly, the empirical results also validate that our proposed system can continuously provide accurate recommendation over time by capturing users’ changing interests.


  1. 1.
  2. 2.
    Cao, H., Chen, E., Yang, J., Xiong, H.: Enhancing recommender systems under volatile userinterest drifts. In: CIKM 2009, pp. 1257–1266. ACM, New York, NY, USA (2009)Google Scholar
  3. 3.
    de Macedo, A.Q., Marinho, L.B.: Event recommendation in event-based social networks. In: HT 2014, ACM (2014)Google Scholar
  4. 4.
    Gomez Rodriguez, M., Rogati, M.: Bridging offline and online social graph dynamics. In: CIKM 2012, pp. 2447–2450. ACM, New York, NY, USA (2012)Google Scholar
  5. 5.
    Khrouf, H., Troncy, R.: Hybrid event recommendation using linked data and user diversity. In: RecSys 2013, pp. 185–192. ACM, New York, NY, USA (2013)Google Scholar
  6. 6.
    Li, X., Guo, L., Zhao, Y.E.: Tag-based social interest discovery. In: WWW 2008, pp. 675–684. ACM, New York, NY, USA (2008)Google Scholar
  7. 7.
    Liu, B., Xiong, H.: Point-of-interest recommendation in location based social networks with topic and location awareness. In: SDM, pp. 396–404. SIAM (2013)Google Scholar
  8. 8.
    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 2012, pp. 1032–1040. ACM, New York, NY, USA (2012)Google Scholar
  9. 9.
    Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: WSDM 2011, pp. 287–296. ACM, New York, NY, USA (2011)Google Scholar
  10. 10.
    Qiao, Z., Zhang, P., Zhou, C., Cao, Y., Guo, L., Zhang, Y.: Event recommendation in event-based social networks. In: AAAI 2014 (2014)Google Scholar
  11. 11.
    Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: NIPS 2008, vol. 20 (2008)Google Scholar
  12. 12.
    Yang, X., Steck, H., Liu, Y.: Circle-based recommendation in online social networks. In: KDD 2012, pp. 1267–1275. ACM, New York, NY, USA (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Cailing Dong
    • 1
  • Yilin Shen
    • 2
  • Bin Zhou
    • 1
    Email author
  • Hongxia Jin
    • 2
  1. 1.University of MarylandBaltimore CountyUSA
  2. 2.Samsung Research AmericaSan JoseUSA

Personalised recommendations