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Analyzing Location Predictability on Location-Based Social Networks

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8443)

Abstract

With the growing popularity of location-based social networks, vast amount of user check-in histories have been accumulated. Based on such historical data, predicting a user’s next check-in place is of much interest recently. There is, however, little study on the limit of predictability of this task and its correlation with users’ demographics. These studies can give deeper insight to the prediction task and bring valuable insights to the design of new prediction algorithms. In this paper, we carry out a thorough study on the limit of check-in location predictability, i.e., to what extent the next locations are predictable, in the presence of special properties of check-in traces. Specifically, we begin with estimating the entropy of an individual check-in trace and then leverage Fano’s inequality to transform it to predictability. Extensive analysis has then been performed on two large-scale check-in datasets from Jiepang and Gowalla with 36M and 6M check-ins, respectively. As a result, we find 25% and 38% potential predictability respectively. Finally, the correlation analysis between predictability and users’ demographics has been performed. The results show that the demographics, such as gender and age, are significantly correlated with location predictability.

Keywords

Location predictability entropy LBSN 

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References

  1. 1.
    Chang, J., Sun, E.: Location3: How users share and respond to location-based data on social. In: Proc. of ICWSM 2011 (2011)Google Scholar
  2. 2.
    Noulas, A., Scellato, S., Lathia, N., Mascolo, C.: Mining user mobility features for next place prediction in location-based services. In: Proc. of ICDM 2012, pp. 1038–1043. IEEE (2012)Google Scholar
  3. 3.
    Gao, H., Tang, J., Liu, H.: Exploring social-historical ties on location-based social networks. In: Proc. of ICWSM 2012 (2012)Google Scholar
  4. 4.
    Song, C., Qu, Z., Blumm, N., Barabási, A.: Limits of predictability in human mobility. Science 327(5968), 1018–1021 (2010)CrossRefzbMATHMathSciNetGoogle Scholar
  5. 5.
    Fano, R.: Transmission of information: a statistical theory of communications. M.I.T. Press (1961)Google Scholar
  6. 6.
    Jensen, B., Larsen, J., Jensen, K., Larsen, J., Hansen, L.: Estimating human predictability from mobile sensor data. In: IEEE International Workshop on Machine Learning for Signal Processing (MLSP), pp. 196–201. IEEE (2010)Google Scholar
  7. 7.
    Lin, M., Hsu, W., Lee, Z.: Predictability of individuals’ mobility with high-resolution positioning data. In: Proc. of Ubicomp 2012, pp. 381–390. ACM (2012)Google Scholar
  8. 8.
    Yan, X.Y., Han, X.P., Wang, B.H., Zhou, T.: Diversity of individual mobility patterns and emergence of aggregated scaling laws. Scientific Reports 3 (2013)Google Scholar
  9. 9.
    Cho, E., Myers, S., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: Proc. of KDD 2011, pp. 1082–1090 (2011)Google Scholar
  10. 10.
    Kontoyiannis, I., Algoet, P., Suhov, Y., Wyner, A.: Nonparametric entropy estimation for stationary processes and random fields, with applications to English text. IEEE Transactions on Information Theory 44(3), 1319–1327 (1998)CrossRefzbMATHMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.University of Science and Technology of ChinaChina
  2. 2.Microsoft ResearchChina
  3. 3.Hong Kong University of Science and TechnologyHong KongChina

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