Advertisement

Toward the New Item Problem: Context-Enhanced Event Recommendation in Event-Based Social Networks

  • Zhenhua Wang
  • Ping He
  • Lidan Shou
  • Ke Chen
  • Sai Wu
  • Gang Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9022)

Abstract

Increasing popularity of event-based social networks (EBSNs) calls for the developments in event recommendation techniques. However, events are uniquely different from conventional recommended items because every event to be recommended is a new item. Traditional recommendation methods such as collaborative filtering techniques, which rely on users’ rating histories, are not suitable for this problem. In this paper, we propose a novel context-enhanced event recommendation method, which exploits the rich context in EBSNs by unifying content, social and geographical information. Experiments on a real-world dataset show promising results of the proposed method.

Keywords

Event recommendation event-based social network new item problem learning to rank 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. TKDE 17(6), 734–749 (2005)Google Scholar
  2. 2.
    Cao, Y., Xu, J., Liu, T.-Y., Li, H., Huang, Y., Hon, H.-W.: Adapting ranking svm to document retrieval. In: SIGIR, pp. 186–193 (2006)Google Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    Troncy, R., Fialho, A.T.S., Hardman, L., Saathoff, C.: Experiencing events through user-generated media. In: Proceedings of the First International Workshop on Consuming Linked Data (2010)Google Scholar
  6. 6.
    Yang, X., Steck, H., Guo, Y., Liu, Y.: On top-k recommendation using social networks. In: RecSys, pp. 67–74 (2012)Google Scholar
  7. 7.
    Ye, M., Yin, P., Lee, W.-C., Lee, D.-L.: Exploiting geographical influence for collaborative point-of-interest recommendation. In: SIGIR, pp. 325–334 (2011)Google Scholar
  8. 8.
    Yuan, Q., Cong, G., Ma, Z., Sun, A., Magnenat-Thalmann, N.: Time-aware point-of-interest recommendation. In: SIGIR, pp. 363–372 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Zhenhua Wang
    • 1
    • 2
  • Ping He
    • 2
  • Lidan Shou
    • 2
  • Ke Chen
    • 2
  • Sai Wu
    • 2
  • Gang Chen
    • 2
  1. 1.Huawei TechnologiesHangzhouChina
  2. 2.College of Computer Science and TechnologyZhejiang UniversityHangzhouChina

Personalised recommendations