Short term studies in controlled environments have shown that user behaviour is consistent enough to predict disruptive smartphone notifications. However, in practice, user behaviour changes over time (concept drift) and individual user preferences need to be considered. There is a lack of research on which methods are best suited for predicting disruptive smartphone notifications longer-term, taking into account varying error costs. In this paper we report on a 16 week field study comparing how well different learners perform at mitigating disruptive incoming phone calls.

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In machine learning, concept drift can cause the optimal solution to a given problem to change as time passes, leading to less accurate predictions. Concept drift can be sudden, gradual or reoccuring. Understanding the consequences of concept drift is particularly important in human-centric applications where changes in the underlying data and environment are common and unexpected. In order to gain a better understanding of the adverse effects of different types of concept drift on learners, we propose a novel simulation tool that is able to incrementally generate datasets with customisable concept drift by interacting with a human in a game-like setting. We illustrate our approach by generating and analysing concept drift simulations inspired by body-sensor based long-term activity recognition. Our initial results show that current unsupervised adaptation techniques can be caught in cyclic mislabelling and that a hybrid solution that is selfcalibrating and semi-supervised is more robust than any of the two taken separately for this example.