Smartphone notifications frequently interrupt our daily lives, often at inopportune moments. We propose the decision-on-information-gain model, which extends the existing data collection convention to capture a range of interruptibility behaviour implicitly. Through a six-month in-the-wild study of 11,346 notifications, we find that this approach captures up to 125% more interruptibility cases. Secondly, we find different correlating contextual features for different behaviour using the approach and find that predictive models can be built with >80% precision for most users. However we note discrepancies in performance across labelling, training, and evaluation methods, creating design considerations for future systems.
Turner et al. REF proposed a decision-on-information-gain model to understand the users' microdecisions against notifications.
Reachable but not Receptive : Enhancing Smartphone Interruptibility Prediction by Modelling the Extent of User Engagement with Notifications
Pervasive Mob. Comput.
Pervasive Mob. Comput.
Computer Science