Xiaoxing Zhao
ABSTRACT
In the era of mobile Internet, a vast amount of geo-spatial data allows us to gain further insights into human activities, which is critical for Internet Services Providers (ISP) to provide better personalized services. With the pervasiveness of mobile Internet, much evidence show that human mobility has heavy impact on app usage behavior. In this paper, we propose a method based on machine learning to predict users' app usage behavior using several features of human mobility extracted from geo-spatial data in mobile Internet traces. The core idea of our method is selecting a set of mobility attributes (e.g. location, travel pattern, and mobility indicators) that have large impact on app usage behavior and inputting them into a classification model. We evaluate our method using real-world network traffic collected by our self-developed high-speed Traffic Monitoring System (TMS). Our prediction method achieves 90.3% accuracy in our experiment, which verifies the strong correlation between human mobility and app usage behavior. Our experimental results uncover a big potential of geo-spatial data extracted from mobile Internet.
- Y. Zhang, "User mobility from the view of cellular data networks," in IEEE INFOCOM, 2014.Google Scholar
- R. Baeza-Yates, D. Jiang, and F. Silvestri, "Predicting the next app that you are going to use," in Proceedings of ACM WSDM 2015, March 2015. Google ScholarDigital Library
- C. Shin, J. H. Hong, and A. K. Dey, "Understanding and prediction of mobile application usage for smart phones," in Proceedings of ACM UbiComp, 2012. Google ScholarDigital Library
- K. Huang, C. Zhang, X. Ma, and G. Chen, "Predicting mobile application usage using contextual information," in Proceedings of the 2012 ACM Conference on Ubiquitous Computing, September 2012. Google ScholarDigital Library
- T. Chang, Q. Liu, and E. Chen, "Prediction for mobile application usage patterns," Nokia MDC Workshop, vol. 12, June 2012.Google Scholar
- Z.-X. Liao, S.-C. Li, W.-C. Peng, P. S. Yu, and T.-C. Liu, "On the feature discovery for app usage prediction in smartphones," in Data Mining (ICDM). 2013 IEEE 13th International Conference on, December 2013.Google Scholar
- J. Liu, F. Liu, and N. Ansari, "Monitoring and analyzing big traffic data of a large-scale cellular network with hadoop," Network, IEEE, vol. 28, no. 4, 2014.Google Scholar
- M. Zaharia, P. Wendell, A. Konwinski, and H. Karau, Learning Spark. O'Reilly Media, Inc., 2015.Google Scholar
- R. S. Xin, J. Rosen, M. Zaharia, and M. J, "Shark: Sql and rich analytics at scale," in ACM SIGMOD, 2013. Google ScholarDigital Library
- S. Liu, Q. Kang, and J. An, "A weight-incorporated similarity-based clustering ensemble method," in 11th IEEE International Conference on Networking, Sensing and Control (ICNSC), 2014.Google Scholar
- A. Thiagarajan, L. Ravindranath, and H. Balakrishnan, "Accurate, low-energy trajectory mapping for mobile devices," in NSDI, March 2011. Google ScholarDigital Library
- J. Yang, X. Zhang, and Y. Qiao, "Global and individual mobility pattern discovery based on hotspots," in IEEE International Conference on Communications (ICC), 2015.Google Scholar
- D. Brockmann, L. Hufnagel, and T. Geisel, "The scaling laws of human travel," Nature, vol. 439, pp. 462--465, Jaunary 2006.Google ScholarCross Ref
- C. Song, T. Koren, and P. Wang, "Modelling the scaling properties of human mobility," Nature Physics, vol. 6, no. 10, May 2010.Google Scholar
- L. Breiman, "Random forests," Machine learning, vol. 45, no. 1, October 2011. Google ScholarDigital Library
- T. U. Md and M. A. Uddiny, "A guided random forest based feature selection approach for activity recognition," in International Conference on Electrical Engineering and Information Communication Technology, 2015.Google Scholar
- J. Huang and C. X. Ling, "Using auc and accuracy in evaluating learning algorithms," IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 3, 2005. Google ScholarDigital Library
- I. Trestian, S. Ranjan, and A. Kuzmanovic, "Measuring serendipity: connecting people, locations and interests in a mobile 3g network," in Proceedings of IMC 2009, March 2009. Google ScholarDigital Library
- M. Böhmer, B. Hecht, and J. Schãűning, "Falling asleep with angry birds, facebook and kindle: a large scale study on mobile application usage," in Proceedings of MobileHCI 11. ACM, March 2011. Google ScholarDigital Library
- L. Meng, S. Liu, and A. Striegel, "Analyzing the longitudinal impact of proximity, location, and personality on smartphone usage," Computational Social Networks, vol. 1, no. 1, May 2014.Google Scholar
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