Abstract
We contribute a method for approximating users’ interruptibility costs to use for experience sampling and validate the method in an application that learns when to automatically turn off and on the phone volume to avoid embarrassing phone interruptions. We demonstrate that users have varying costs associated with interruptions which indicates the need for personalized cost approximations. We compare different experience sampling techniques to learn users’ volume preferences and show those that ask when our cost approximation is low reduce the number of embarrassing interruptions and result in more accurate volume classifiers when deployed for long-term use.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Cohn, D., Atlas, L., Ladner, R.: Improving Generalization with Active Learning. Machine Learning 15(2), 201–221 (1994)
Csikszentmihalyi, M., Larson, R.: Validity and Reliability of the Experience Sampling Method. Journal of Nervous and Mental Disease 175(9), 526–536 (1987)
Czerwinski, M., Horvitz, E., Wilhite, S.: A Diary Study of Task Switching and Interruptions. In: Proc. of CHI 2004, pp. 175–182 (2004)
Donmez, P., Carbonell, J.G.: Proactive learning: cost- sensitive active learning with multiple imperfect oracles. In: Proc. of the Conference on Information and Knowledge Management (CIKM), pp. 619–628, 2008.
Faulring, A., Myers, B., Mohnkern, K., Schmerl, B., Steinfeld, A., Zimmerman, J., Smailagic, A., Hansen, J., Siewiorek, D.: Agent-assisted Task Management that Reduces Email Overload. In: Proc. of IUI 2010, pp. 61–70 (2010)
Fogarty, J., Hudson, S.E., Atkeson, C.G., Avrahami, D., Forlizzi, J., Kiesler, S., Lee, J.C., Yang, J.: Predicting Human Interruptibility with Sensors. ACM Trans. Computer-Human Interaction 12(1), 119–146 (2005)
Ho, J., Intille, S.S.: Using Context-Aware Computing to Reduce the Perceived Burden of Interruptions from Mobile Devices. In: Proc. of CHI 2005, pp. 909–918 (2005)
Horvitz, E.: Principles of Mixed-Initiative User Interfaces. In: Proc. of CHI 1999, pp. 159–166 (1999)
Horvitz, E., Apacible, J.: Learning and Reasoning about Interruption. In: Proc. of the International Conference on Multimodal Interfaces (ICMI), pp. 20–27 (2003)
Horvitz, E., Koch, P., Apacible, J.: BusyBody: Creating and Fielding Personalized Models of the Cost of Interruption. In: Proc. of Conference on Computer Supported Cooperative Work (CSCW), pp. 507–510 (2004)
Intille, S.S., Rondoni, J., Kukla, C., Anacona, I., Bao, L.: A Context-Aware Experience Sampling Tool. Extended Abstract in Proceedings of CHI, pp. 972–973 (2003)
Kapoor, A., Horvitz, E.: On Discarding, Caching, and Recalling Samples in Active Learning. In: Proc. of Uncertainty in Artificial Intelligence (UAI), pp. 209–216 (2007)
Kapoor, A., Horvitz, E.: Experience Sampling for Building Predictive User Models: a Comparative Study. In: Proc. of CHI 2008, pp. 657–666 (2008)
Kern, N., Schiele, B.: Towards Personalized Mobile Interruptibility Estimation. In: Hazas, M., Krumm, J., Strang, T. (eds.) LoCA 2006. LNCS, vol. 3987, pp. 134–150. Springer, Heidelberg (2006)
Khalil, A., Connelly, K.: Improving cell phone awareness by using calendar information. In: Costabile, M.F., Paternó, F. (eds.) INTERACT 2005. LNCS, vol. 3585, pp. 588–600. Springer, Heidelberg (2005)
Krishnan, M.V.: Availability and Mobile Phone Interruptions: Examining the Role of Technology in Coordinating Mobile Calls. Masters Thesis MCS-2008:18, Blekinge Institute of Technology (March 2008)
Lewis, D.D., Catlett, J.: Heterogeneous Uncertainty Sampling for Supervised Learning. In: Proc. of the International Conference on Machine Learning (ICML), pp. 148–156 (1994)
Lewis, D.D., Gale, W.A.: A Sequential Algorithm for Training Text Classifiers. In: Proc of the Conference on Research and Development in Information Retrieval (SIGIR), pp. 3–12 (1994)
Lim, B.Y., Dey, A.K., Avrahami, D.: Why and Why Not Explanations Improve the Intelligibility of Context-Aware Intelligent Systems. In: Proc. of CHI 2009, pp. 2119–2128 (2009)
McFarlane, D.: Coordinating the interruption of people in human-computer interaction. In: Proc. of International Conference on Human-Computer Interaction (INTERACT), pp. 295–303 (1999)
Milewski, A.E., Smith, T.M.: Providing Presence Cues to Telephone Users. In: Proc. of the Conference on Computer Supported Cooperative Work (CSCW), pp. 89–96 (2000)
Scollon, C., Kim-Prieto, C., Diener, E.: Experience Sampling: Promise and Pitfalls, Strengths and Weaknesses. Journal of Happiness Studies 4, 5–34 (2003)
Shadbolt, N., Burton, A.M.: The Empirical Study of Knowledge Elicitation Techniques. SIGART Bulletin 108, 15–18 (1989)
Schmidt, A., Takaluoma, A., Mäntyjärvi, J.: Context-Aware Telephony Over WAP. Personal and Ubiquitous Computing 4(4), 225–229 (2000)
Siewiorek, D.P., Smailagic, A., Furukawa, J., Krause, A., Moraveji, N., Reiger, K., Shaffer, J., Wong, F.L.: SenSay: a Context-Aware Mobile Phone. In: Proc. of the International Symposium on Wearable Computers, pp. 248–249 (2003)
Stumpf, S., Rajaram, V., Li, L., Burnett, M., Dietterich, T., Sullivan, E., Drummond, R., Herlocker, J.: Toward harnessing user feedback for machine learning. In: Proc of IUI 2007, pp. 82–91 (2007)
Toninelli, A., Khushraj, D., Lassila, O., Montanari, R.: Towards Socially Aware Mobile Phones. In: Proc. of the Social Data on the Web Workshop (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rosenthal, S., Dey, A.K., Veloso, M. (2011). Using Decision-Theoretic Experience Sampling to Build Personalized Mobile Phone Interruption Models. In: Lyons, K., Hightower, J., Huang, E.M. (eds) Pervasive Computing. Pervasive 2011. Lecture Notes in Computer Science, vol 6696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21726-5_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-21726-5_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21725-8
Online ISBN: 978-3-642-21726-5
eBook Packages: Computer ScienceComputer Science (R0)