Predicting Phone Usage Behaviors with Sensory Data Using a Hierarchical Generative Model

  • Chuankai AnEmail author
  • Dan Rockmore
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9794)


Using a sizable set of sensory data and related usage records on Android devices, we are able to give a reasonable prediction of three imporant aspects of phone usage: messages, phone calls and cellular data. We solve the problem via an estimation of a user’s daily routine, on which we can train a hierarchical generative model on phone usages in all time slots of a day. The model generates phone usage behaviors in terms of three kinds of data: the state of user-phone interaction, occurrence times of an activity and the duration of the activity in each occurrence. We apply the model on a dataset with 107 frequent users, and find the prediction error of generative model is the smallest when compare with several other baseline methods. In addition, CDF curves illustrate the availability of generative model for most users with the distribution of prediction error for all test cases. We also explore the effects of time slots in a day, as well as size of training and test sets. The results suggest several interesting directions for further research.


Phone usage prediction Generative model 


  1. 1.
    Cho, J., Garcia-Molina, H.: Estimating frequency of change. ACM Trans. Internet Technol. (TOIT) 3(3), 256–290 (2003)CrossRefGoogle Scholar
  2. 2.
    Do, T.M.T., Blom, J., Gatica-Perez, D.: Smartphone usage in the wild: a large-scale analysis of applications and context. In: ICMI, pp. 353–360. ACM (2011)Google Scholar
  3. 3.
    Eckmann, J., Moses, E., Sergi, D.: Entropy of dialogues creates coherent structures in e-mail traffic. Proc. Nat. Acad. Sci. USA 101(40), 14333–14337 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Falaki, H., Mahajan, R., Kandula, S., Lymberopoulos, D., Govindan, R., Estrin, D.: Diversity in smartphone usage. In: MobiSys, pp. 179–194. ACM (2010)Google Scholar
  5. 5.
    Farrahi, K., Gatica-Perez, D.: What did you do today?: discovering daily routines from large-scale mobile data. In: Proceedings of MM, pp. 849–852. ACM (2008)Google Scholar
  6. 6.
    Farrahi, K., Gatica-Perez, D.: Probabilistic mining of socio-geographic routines from mobile phone data. Sel. Top. Signal Process. 4(4), 746–755 (2010)CrossRefGoogle Scholar
  7. 7.
    Ferraz Costa, A., Yamaguchi, Y., Juci Machado Traina, A., Traina Jr., C., Faloutsos, C.: RSC: Mining and modeling temporal activity in social media. In: KDD, pp. 269–278. ACM (2015)Google Scholar
  8. 8.
    Hidalgo, R.C.A.: Conditions for the emergence of scaling in the inter-event time of uncorrelated and seasonal systems. Phys. A: Stat. Mech. Appl. 369(2), 877–883 (2006)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Hollmén, J., Tresp, V.: Call-based fraud detection in mobile communication networks using a hierarchical regime-switching model. In: Advances in Neural Information Processing Systems, pp. 889–895 (1999)Google Scholar
  10. 10.
    Jin, Y., et al.: Characterizing data usage patterns in a large cellular network. In: SIGCOMM Workshop on Cellular Networks, pp. 7–12. ACM (2012)Google Scholar
  11. 11.
    Juan, D.-C., Li, L., Peng, H.-K., Marculescu, D., Faloutsos, C.: Beyond poisson: modeling inter-arrival time of requests in a datacenter. In: Tseng, V.S., Ho, T.B., Zhou, Z.-H., Chen, A.L.P., Kao, H.-Y. (eds.) PAKDD 2014, Part II. LNCS, vol. 8444, pp. 198–209. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  12. 12.
    Kang, J.M., Seo, S.S., Hong, J.W.K.: Usage pattern analysis of smartphones. In: Network Operations and Management Symposium, pp. 1–8. IEEE (2011)Google Scholar
  13. 13.
    Kleinberg, J.: Bursty and hierarchical structure in streams. Data Min. Knowl. Disc. 7(4), 373–397 (2003)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Liao, Z.X., Lei, P.R., Shen, T.J., Li, S.C., Peng, W.C.: Mining temporal profiles of mobile applications for usage prediction. In: ICDMW, pp. 890–893. IEEE (2012)Google Scholar
  15. 15.
    Liao, Z.X., Li, S.C., Peng, W.C., Yu, P.S., Liu, T.C.: On the feature discovery for app usage prediction in smartphones. In: ICDM, pp. 1127–1132. IEEE (2013)Google Scholar
  16. 16.
    Liao, Z.X., Pan, Y.C., Peng, W.C., Lei, P.R.: On mining mobile apps usage behavior for predicting apps usage in smartphones. In: CIKM, pp. 609–618. ACM (2013)Google Scholar
  17. 17.
    Malmgren, R.D., Hofman, J.M., Amaral, L.A., Watts, D.J.: Characterizing individual communication patterns. In: KDD, pp. 607–616. ACM (2009)Google Scholar
  18. 18.
    Malmgren, R.D., Stouffer, D.B., Motter, A.E., Amaral, L.A.: A poissonian explanation for heavy tails in e-mail communication. Proc. Nat. Acad. Sci. 105(47), 18153–18158 (2008)CrossRefGoogle Scholar
  19. 19.
    Vaz de Melo, P.O.S., Faloutsos, C., Assunção, R., Loureiro, A.: The self-feeding process: a unifying model for communication dynamics in the web. In: WWW, pp. 1319–1330 (2013)Google Scholar
  20. 20.
    Melo, P.O., Faloutsos, C., Assunçao, R., Alves, R., Loureiro, A.A.: Universal and distinct properties of communication dynamics: how to generate realistic inter-event times. TKDD 9(3), 24 (2015)CrossRefGoogle Scholar
  21. 21.
    Shin, C., Hong, J.H., Dey, A.K.: Understanding and prediction of mobile application usage for smart phones. In: Ubicomp, pp. 173–182. ACM (2012)Google Scholar
  22. 22.
    Sia, K.C., Cho, J., Cho, H.K.: Efficient monitoring algorithm for fast news alerts. IEEE Trans. Knowl. Data Eng. 19(7), 950–961 (2007)CrossRefGoogle Scholar
  23. 23.
    Verkasalo, H.: Analysis of smartphone user behavior. In: Mobile Business and 2010 Ninth Global Mobility Roundtable (ICMB-GMR), pp. 258–263. IEEE (2010)Google Scholar
  24. 24.
    Wagner, D.T., Rice, A., Beresford, A.R.: Device analyzer: large-scale mobile data collection. ACM SIGMETRICS Perform. Eval. Rev. 41(4), 53–56 (2014)CrossRefGoogle Scholar
  25. 25.
    Wagner, D.T., Rice, A., Beresford, A.R.: Device analyzer: understanding smartphone usage. In: Stojmenovic, I., Cheng, Z., Guo, S. (eds.) MOBIQUITOUS 2013. LNICST, vol. 131, pp. 195–208. Springer, Heidelberg (2014)Google Scholar
  26. 26.
    Xu, Y., et al.: Preference, context and communities: a multi-faceted approach to predicting smartphone app usage patterns. In: ISWC, pp. 69–76. ACM (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Computer Science DepartmentDartmouth CollegeHanoverUSA

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