Title: Continual Prediction of Notification Attendance with Classical and Deep Network Approaches,
Abstract: We investigate to what extent mobile use patterns can predict -- at the
moment it is posted -- whether a notification will be clicked within the next
10 minutes. We use a data set containing the detailed mobile phone usage logs
of 279 users, who over the course of 5 weeks received 446,268 notifications
from a variety of apps. Besides using classical gradient-boosted trees, we
demonstrate how to make continual predictions using a recurrent neural network
(RNN). The two approaches achieve a similar AUC of ca. 0.7 on unseen users,
with a possible operation point of 50% sensitivity and 80% specificity
considering all notification types (an increase of 40% with respect to a
probabilistic baseline). These results enable automatic, intelligent handling
of mobile phone notifications without the need for user feedback or
personalization. Furthermore, they showcase how forego feature-extraction by
using RNNs for continual predictions directly on mobile usage logs. To the best
of our knowledge, this is the first work that leverages mobile sensor data for
continual, context-aware predictions of interruptibility using deep neural
networks.