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. © 2011 Springer-Verlag.
CITATION STYLE
Rosenthal, S., Dey, A. K., & Veloso, M. (2011). Using decision-theoretic experience sampling to build personalized mobile phone interruption models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6696 LNCS, pp. 170–187). https://doi.org/10.1007/978-3-642-21726-5_11
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