Abstract
This paper proposes a model for the prediction of the next behavior based on the smartphone call record of a user. The data of calls includes a lot of information in addition to time point and talk time. This paper systematically classifies this information and suggests a complex model to predict the next behavior of a user. The call data has a significant meaning by its nature in the frequency analysis, trend analysis, and pattern analysis, and the data is specifically classified into the 30 items and applied to the analysis. The prediction model suggested by this paper collected and arranged the 1106 data of 3 months from the users participated in the experiment, and verified using the 100 prediction data. It showed average 85.06% of accuracy, and according to the survey simultaneously conducted, the users showed the result of being satisfied with the accuracy.
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References
Raento, M., Oulasvirta, A., Petit, R., Toivonen, H.: ContextPhone: A prototyping platform for context-aware mobile applications. IEEE Pervasive Computing 4(2), 51–59 (2005)
Min, J.K., Cho, S.B.: Mobile Human Network Management and Recommendation by Probabilistic Social Mining. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics (2011)
Manavoglu, E., Pavlov, D., Giles, C.L.: Probabilistic User Behavior Models. In: Third IEEE International Conference on Data Mining, ICDM 2003 (November 2003)
Conner, M., Sparks, P.: Extending the Theory of Planned Behavior: A Review and Avenues for Further Research. Journal of Applied Social Psychology 28, 1429–1464 (1998)
Kim, G.S., Kim, D.M., Yoon, T.P., Lee, J.H.: Intention-Awareness Method using Behavior Model Based User Intention. In: Proceedings of KFIS Autumn Conference 2007, vol. 17(2) (2007)
Krause, A., Smailagic, A., Siewiorek, D.P.: Context-aware mobile computing: Learning context-dependent personal preferences from a wearable sensor array. IEEE Trans. on Mobile Computing 5(2), 113–127 (2006)
Cucchiara, R., Grana, C., Prati, A., Vezzani, R.: Probabilistic Posture Classification for Human-Behavior Analysis. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans 35(1) (January 2005)
Farrahi, K., Gatica-Perez, D.: Probabilistic Mining of Socio-Geographic Routines From Mobile Phone Data. IEEE Journal of Selected Topics in Signal Processing (2010)
Eagle, N., Pentland, A.: Reality mining: Sensing complex social systems. J. of Personal and Ubiquitous Computing 10(4), 255–268 (2005)
Lee, Y.S., Cho, S.B.: Similarity Calculation for Mobile Life Log Data Mining. In: Proc. of the KIISE Korea Computer Congress 2011, vol. 38(1(A)) (2011)
Lee, Y.S., Jung, M.C., Cho, S.B.: Collection and construction of user’s context in smart phone. In: Proc. of KCC, vol. 33(1(B)), pp. 115–117 (2006)
Fishbein, M., Ajzen, I.: Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Addison-Wesley, Reading (1975)
Triandis, H.C.: Values, Attitudes, and Interpersonal Behavior. In: Nebraska Symposium on Motivation, vol. 27, pp. 195–259 (1980)
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Kang, S., Won, H., Jeon, G., Lee, YS. (2012). Call Prediction Model Based on Smartphone Users Behavior. In: Lee, G., Howard, D., Ślęzak, D., Hong, Y.S. (eds) Convergence and Hybrid Information Technology. ICHIT 2012. Communications in Computer and Information Science, vol 310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32692-9_24
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DOI: https://doi.org/10.1007/978-3-642-32692-9_24
Publisher Name: Springer, Berlin, Heidelberg
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