By their apps you shall understand them: mining large-scale patterns of mobile phone usage

ABSTRACT

Mobile phones are becoming more and more widely used nowadays, and people do not use the phone only for communication: there is a wide variety of phone applications allowing users to select those that fit their needs. Aggregated over time, application usage patterns exhibit not only what people are consistently interested in but also the way in which they use their phones, and can help improving phone design and personalized services. This work aims at mining automatically usage patterns from apps data recorded continuously with smartphones. A new probabilistic framework for mining usage patterns is proposed. Our methodology involves the design of a bag-of-apps model that robustly represents level of phone usage over specific times of the day, and the use of a probabilistic topic model that jointly discovers patterns of usage over multiple applications and describes users as mixtures of such patterns. Our framework is evaluated using 230 000+ hours of real-life app phone log data, demonstrates that relevant patterns of usage can be extracted, and is objectively validated on a user retrieval task with competitive performance.

References

1. R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. Addison-Wesley, 1999.
2. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993--1022, 2003.
3. J. Burke, D. Estrin, M. Hansen, A. Parker, N. Ramanathan, S. Reddy, and M. B. Srivastava. Participatory sensing. In WSW at Sensys, pages 117--134, 2006.
4. F. D. Davis. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3):319--339, September 1989.
5. N. Eagle, A. S. Pentland, and D. Lazer. Mobile phone data for inferring social network structure. In H. Liu, J. J. Salerno, and M. J. Young, editors, Social Computing, Behavioral Modeling, and Prediction, pages 79--88. Springer US, 2008.
6. N. Eagle and A. (Sandy) Pentland. Reality mining: sensing complex social systems. Personal Ubiquitous Computing, 10(4):255--268, 2006.
7. K. Farrahi and D. Gatica-Perez. What did you do today?: discovering daily routines from large-scale mobile data. In Proc. MM, pages 849--852, New York, 2008.
8. W. R. Gilks. Markov Chain Monte Carlo In Practice. Chapman and Hall/CRC, 1999.
9. I. Guyon, V. Lemaire, M. Boullé, G. Dror, and D. Vogel. Design and analysis of the kdd cup 2009: fast scoring on a large orange customer database. SIGKDD Explor. Newsl., 11(2):68--76, 2009.
10. T. Hofmann. Probabilistic latent semantic indexing. In Proc. SIGIR, pages 50--57, New York, 1999. ACM.
11. S.-J. Hong, K. Y. Tam, and J. Kim. Mobile data service fuels the desire for uniqueness. Communications of the ACM, 49(9):89--94, 2006.
12. J. Jeon, V. Lavrenko, and R. Manmatha. Automatic image annotation and retrieval using cross-media relevance models. In Proc. SIGIR, pages 119--126, New York, 2003. ACM.
13. N. Kiukkonen, J. Blom, O. Dousse, D. Gatica-Perez, and J. Laurila. Towards rich mobile phone datasets: Lausanne data collection campaign. In Proc. ICPS, Berlin, 2010.
14. R. Kwok. Phoning in data. Nature, pages 959--961, 2009.
15. D. Lazer, A. Pentland, L. Adamic, S. Aral, A. Barabasi, D. Brewer, N. Christakis, N. Contractor, J. Fowler, M. Guttmann, T. Jebara, G. King, M. Macy, D. Roy, and M. V. Alstyne. Computational social science. Science, 323(5915):721--723, 2/6/2009 2009.
16. R. Mika, O. Antti, and E. Nathan. Smartphones: An emerging tool for social scientists. Sociological Methods Research, 37(3):426--454, 2009.
17. Nielsen. Critical mass: The worldwide state of the mobile web. Available at http://www.nielsenmobile.com/documents/CriticalMass.pdf, July 2008.
18. Nokia. Smartphone 360 study: Responding to what the user wants and needs. Press Release, Oct. 2007 available at http://www.nokia.com/NOKIA_COM_1/Press/twwln/press_kit/Nokia_Smartphone360_study_Press_Backgrounder_October_2007.pdf.
19. M. Raento, A. Oulasvirta, R. Petit, and H. Toivonen. Contextphone: A prototyping platform for context-aware mobile applications. IEEE Pervasive Computing, 4(2):51--59, 2005.
20. M. Rosen-Zvi, T. Griffiths, M. Steyvers, and P. Smyth. The author-topic model for authors and documents. In Proc. UAI, pages 487--494, Arlington, Virginia, United States, 2004. AUAI Press.
21. H. Verkasalo. Handset-based analysis of mobile service usage. Doctoral dissertation, Helsinki University of Technology, 2009.
22. H. Verkasalo, C. López-Nicolás, F. J. Molina-Castillo, and H. Bouwman. Analysis of users and non-users of smartphone applications. Telematics and Informatics, 27(3):242--255, 2010.
23. B. Zheng, D. C. M. Jr, and X. Lu. Identifying biological concepts from a protein-related corpus with a probabilistic topic model. BMC Bioinformatics, 7:58, 2006.