Towards Personalized Mobile Interruptibility Estimation

The automatic estimation of the user's current interruptibility is important to seamlessly adapt a device's behaviour to the user's situation. Different people differ in the way they rate their interruptibility. In this paper we investigate three options how to adapt an interruptibility estimation system to a particular user: by finding prototypical users, using experience sampling, or using knowledge of prototypical situations. We have experimentally tested all three approaches on a data set of 94 situations that have been annotated by 24 different users.

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