Advertisement

UrbanHubble: Location Prediction and Geo-Social Analytics in LBSN

  • Roland AssamEmail author
  • Simon Feiden
  • Thomas Seidl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9286)

Abstract

Massive amounts of geo-social data is generated daily. In this paper, we propose UrbanHubble, a location-based predictive analytics tool that entails a broad range of state-of-the-art location prediction and recommendation algorithms. Besides, UrbanHubble consists of a visualization component that depicts the real-time complex interactions of users on a map, the evolution of friendships over time, and how friendship triggers mobility.

Keywords

Matrix Factorization Kernel Density Estimation Collaborative Filter Location Prediction Recommendation Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Assam, R., Seidl, T.: Check-in location prediction using wavelets and conditional random fields. In: ICDM 2014, pp. 713–718 (2014)Google Scholar
  2. 2.
    Chiang, M.-F., Lin, Y.-H., Peng, W.-C., Yu, P.S.: Inferring distant-time location in low-sampling-rate trajectories. In: KDD (2013)Google Scholar
  3. 3.
    Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: KDD 2011, pp. 1082–1090 (2011)Google Scholar
  4. 4.
    Kong, L., Liu, Z., Huang, Y.: Spot: locating social media users based on social network context. In: VLDB 2014, pp. 1681–1684 (2014)Google Scholar
  5. 5.
    Lian, D., Zhao, C., Xie, X., Sun, G., Chen, E., Rui, Y.: Geomf: joint geographical modeling and matrix factorization for point-of-interest recommendation. In: KDD 2014, pp. 831–840 (2014)Google Scholar
  6. 6.
    Lichman, M., Smyth, P.: Modeling human location data with mixtures of kernel densities. In: KDD 2014, pp. 35–44 (2014)Google Scholar
  7. 7.
    Wu, F., Lei, T.K.H., Li, Z., Han, J.: Movemine 2.0: mining object relationships from movement data. In: VLDB 2014, pp. 1613–1616 (2014)Google Scholar
  8. 8.
    Ye, J., Zhu, Z., Cheng, H.: What’s your next move: user activity prediction in location-based social networks. In: SDM 2014, pp. 171–179 (2013)Google Scholar
  9. 9.
    Ye, M., Yin, P., Lee, W.-C., Lee, D.-L.: Exploiting geographical influence for collaborative point-of-interest recommendation. In: SIGIR (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.RWTH Aachen UniversityAachenGermany

Personalised recommendations