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Behavior-based adaptive call predictor

Published:29 September 2011Publication History
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Abstract

Predicting future calls can be the next advanced feature of the next-generation telecommunication networks as the service providers are looking to offer new services to their customers. Call prediction can be useful to many applications such as planning daily schedules, avoiding unwanted communications (e.g. voice spam), and resource planning in call centers. Predicting calls is a very challenging task. We believe that this is an emerging area of research in ambient intelligence where the electronic devices are sensitive and responsive to people's needs and behavior. In particular, we believe that the results of this research will lead to higher productivity and quality of life. In this article, we present a Call Predictor (CP) that offers two new advanced features for the next-generation phones namely “Incoming Call Forecast” and “Intelligent Address Book.” For the Incoming Call Forecast, the CP makes the next-24-hour incoming call prediction based on recent caller's behavior and reciprocity. For the Intelligent Address Book, the CP generates a list of most likely contacts/numbers to be dialed at any given time based on the user's behavior and reciprocity. The CP consists of two major components: Probability Estimator (PE) and Trend Detector (TD). The PE computes the probability of receiving/initiating a call based on the caller/user's calling behavior and reciprocity. We show that the recent trend of the caller/user's calling pattern has higher correlation to the future pattern than the pattern derived from the entire historical data. The TD detects the recent trend of the caller/user's calling pattern and computes the adequacy of historical data in terms of reversed time (time that runs towards the past) based on a trace distance. The recent behavior detection mechanism allows CP to adapt its computation in response to the new calling behaviors. Therefore, CP is adaptive to the recent behavior. For our analysis, we use the real-life call logs of 94 mobile phone users over nine months, which were collected by the Reality Mining Project group at MIT. The performance of the CP is validated for two months based on seven months of training data. The experimental results show that the CP performs reasonably well as an incoming call predictor (Incoming Call Forecast) with false positive rate of 8%, false negative rate of 1%, and error rate of 9%, and as an outgoing call predictor (Intelligent Address Book) with the accuracy of 70% when the list has five entries. The functionality of the CP can be useful in assisting its user in carrying out everyday life activities such as scheduling daily plans by using the Incoming Call Forecast, and saving time from searching for the phone number in a typically lengthy contact book by using the Intelligent Address Book. Furthermore, we describe other useful applications of CP besides its own aforementioned features including Call Firewall and Call Reminder.

References

  1. Aldor-Noiman, S. 2006. Forecasting demand for a telephone call center: Analysis of desired versus attainable precision. Master thesis 2005. Department of Statistics, Technion—Israel Institute of Technology.Google ScholarGoogle Scholar
  2. Brown, L., Gans, N., Mandelbaum, A., Sakov, A., Shen, H., Zeltyn, S., and Zhao, L. 2005. Statistical analysis of a telephone call center: A queeing-science perspective. J. Amer. Stat. Ass. 100, 469, 36--50.Google ScholarGoogle ScholarCross RefCross Ref
  3. Dantu, R. and Kolan, P. 2005. Detecting spam in VoIP networks. In Proceedings of USENIX, SRUTI (Steps for ReducingUnwanted Traffic on the Internet). Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Eagle, N. and Pentland, A. 2005. Social serendipity: Mobilizing social software. IEEE Pervas. Comput. 4, 2. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Eagle, N. and Pentland, A. 2006. Reality mining; Sensing complex social systems. Personal Ubiquit. Comput. 10, 4. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Eagle, N., Pentland, A., and Lazer, D. 2007. Infering social network structure using mobile phone data. Proc. Nat. Acad. Sci. To appear.Google ScholarGoogle Scholar
  7. Harless, C. E. and Kowalski, T. J. 2000. U.S. Patent 6084954, Jul. 4.Google ScholarGoogle Scholar
  8. Jasso, H., Fountain, T., Baru, C., Hodgkiss, W., Reich, D., and Warner, K. 2007. Prediction of 9-1-1 call volumes for emergency event detection. In Proceedings of the 8th Annual International Digital Government Research Conference, vol. 228. 148--154. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Jones, M., Marron, J. S., and Sheather, S. J. 1996. A brief survey of bandwidth selection for density estimation. J. Amer. Statist. Assoc. 433, 91, 401--407.Google ScholarGoogle ScholarCross RefCross Ref
  10. Kolan, P. and Dantu, R. 2007. Socio-Technical defense against voice spamming. ACM Trans. Auton. Adapt. Syst. 2, 1. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Kolan, P. and Dantu, R. 2008. Nuisance level of a voice call. ACM Trans. Multimedia Comput. Comm. Appl. 5, 1. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Leon-Garcia, A. 1994. Probability and Random Processes for Electrical Engineering. 2nd Ed. Addison-Wesley.Google ScholarGoogle Scholar
  13. Massachusetts Institute of Technology. 2007. Reality minning. http://reality.media.mit.eduGoogle ScholarGoogle Scholar
  14. Ntuli, D. 2007. Phone dating takes off in mobile crazy SA. http://mybroadband.co.za/news/Cellular/1019.htmGoogle ScholarGoogle Scholar
  15. Parzen, E. 1962. On estimation of a probability density function and mode. Ann. Math. Statist. 33, 3.Google ScholarGoogle ScholarCross RefCross Ref
  16. Rosenberg, J., Jennings, C., and Paterson, J. 2006. The session initiation protocol (SIP) and spam. Spam Draft-draft-ietfsipping-spam-02.txtGoogle ScholarGoogle Scholar
  17. Sheather, S. J. and Jones, M. C. 1991. A reliable data-based bandwidth selection method for kernel density estimation. J. Roy. Statist. Soc. B, 53, 683--690.Google ScholarGoogle ScholarCross RefCross Ref
  18. Trendhunter. 2006. Shopping by phone takes off. http://www.trendhunter.com/trends/shopping-by-phone-takes-off/Google ScholarGoogle Scholar
  19. Wand, M. P. and Jones, M. C. 1994. Multivariate plug-in bandwidth selection. Comput. Statist. 9, 97--117.Google ScholarGoogle Scholar
  20. Web japan. 2006. Reading on the move. http://web-japan.org/trends/business/bus061211.htmlGoogle ScholarGoogle Scholar

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            cover image ACM Transactions on Autonomous and Adaptive Systems
            ACM Transactions on Autonomous and Adaptive Systems  Volume 6, Issue 3
            September 2011
            150 pages
            ISSN:1556-4665
            EISSN:1556-4703
            DOI:10.1145/2019583
            Issue’s Table of Contents

            Copyright © 2011 ACM

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 29 September 2011
            • Accepted: 1 August 2010
            • Received: 1 September 2009
            Published in taas Volume 6, Issue 3

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