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Predicting Replacement of Smartphones with Mobile App Usage

  • Dun Yang
  • Zhiang WuEmail author
  • Xiaopeng Wang
  • Jie Cao
  • Guandong Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10041)

Abstract

To identify right customers who intend to replace the smartphone can help to perform precision marketing and thus bring significant financial gains to cellphone retailers. In this paper, we provide a study of exploiting mobile app usage for predicting users who will change the phone in the future. We first analyze the characteristics of mobile log data and develop the temporal bag-of-apps model, which can transform the raw data to the app usage vectors. We then formularize the prediction problem, present the hazard based prediction model, and derive the inference procedure. Finally, we evaluate both data model and prediction model on real-world data. The experimental results show that the temporal usage data model can effectively capture the unique characteristics of mobile log data, and the hazard based prediction model is thus much more effective than traditional classification methods. Furthermore, the hazard model is explainable, that is, it can easily show how the replacement of smartphones relate to mobile app usage over time.

Keywords

App usage Smartphone replacement Hazard model Mobile log data 

Notes

Acknowledgments

This research was partially supported by National Natural Science Foundation of China under Grants 71571093, 71372188 and 61502222, National Center for International Joint Research on E-Business Information Processing under Grant 2013B0135, National Key Research and Development Program of China under Grant 2016YFB1000901, and Industry Projects in Jiangsu S&T Pillar Program under Grant BE2014141.

References

  1. 1.
    Böhmer, M., Hecht, B., et al.: Falling asleep with angry birds, Facebook and kindle: a large scale study on mobile application usage. In: MobileHCI, pp. 47–56 (2011)Google Scholar
  2. 2.
    Böhning, D.: Multinomial logistic regression algorithm. Ann. Inst. Stat. Math. 44(1), 197–200 (1992)CrossRefzbMATHGoogle Scholar
  3. 3.
    Buckinx, W., Van den Poel, D.: Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting. EJOR 164(1), 252–268 (2005)CrossRefzbMATHGoogle Scholar
  4. 4.
    Cox, D.R.: Regression models and life-tables. In: Kotz, S., Johnson, N.L. (eds.) Breakthroughs in Statistics. Springer, New York (1992)Google Scholar
  5. 5.
    Do, T.M.T., Gatica-Perez, D.: By their apps you shall understand them: mining large-scale patterns of mobile phone usage. In: MUM (2010)Google Scholar
  6. 6.
    Ghose, A., Han, S.P.: An empirical analysis of user content generation and usage behavior on the mobile internet. Manag. Sci. 57(9), 1671–1691 (2011)CrossRefGoogle Scholar
  7. 7.
    Kapoor, K., Sun, M., Srivastava, J., Ye, T.: A hazard based approach to user return time prediction. In: KDD, pp. 1719–1728 (2014)Google Scholar
  8. 8.
    Parate, A., Böhmer, M., Chu, D., et al.: Practical prediction and prefetch for faster access to applications on mobile phones. In: UbiComp, pp. 275–284 (2013)Google Scholar
  9. 9.
    Shi, Y., Karatzoglou, A., Baltrunas, L., Larson et al.: TFMAP: optimizing map for top-n context-aware recommendation. In: SIGIR, pp. 155–164 (2012)Google Scholar
  10. 10.
    Shin, C., Hong, J.H., Dey, A.K.: Understanding and prediction of mobile application usage for smart phones. In: UbiComp, pp. 173–182 (2012)Google Scholar
  11. 11.
    Xie, Y., Li, X., Ngai, E., Ying, W.: Customer churn prediction using improved balanced random forests. Expert Syst. Appl. 36(3), 5445–5449 (2009)CrossRefGoogle Scholar
  12. 12.
    Yang, J., Wei, X., et al.: Activity lifespan: an analysis of user survival patterns in online knowledge sharing communities. ICWSM 10, 186–193 (2010)Google Scholar
  13. 13.
    Yuan, B., Xu, B., Chung, T., Shuai, K., Liu, Y.: Mobile phone recommendation based on phone interest. In: Benatallah, B., Bestavros, A., Manolopoulos, Y., Vakali, A., Zhang, Y. (eds.) WISE 2014. LNCS, vol. 8786, pp. 308–323. Springer, Heidelberg (2014). doi: 10.1007/978-3-319-11749-2_24 Google Scholar
  14. 14.
    Zhu, H., Chen, E., Xiong, H., Cao, H., Tian, J.: Mobile app. classification with enriched contextual information. IEEE Trans. Mob. Comput. 13(7), 1550–1563 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Dun Yang
    • 1
  • Zhiang Wu
    • 1
    Email author
  • Xiaopeng Wang
    • 2
  • Jie Cao
    • 1
  • Guandong Xu
    • 3
  1. 1.School of Info. EngineeringNanjing University of Finance and EconomicsNanjingChina
  2. 2.Jiangsu Posts & Telecommunications Planning and Designing InstituteNanjingChina
  3. 3.Advanced Analytics InstituteUniversity of TechnologySydneyAustralia

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