  User engagement is crucial to the long-term success of a mobile app. Several
metrics, such as dwell time, have been used for measuring user engagement.
However, how to effectively predict user engagement in the context of mobile
apps is still an open research question. For example, do the mobile usage
contexts (e.g.,~time of day) in which users access mobile apps impact their
dwell time? Answers to such questions could help mobile operating system and
publishers to optimize advertising and service placement. In this paper, we
first conduct an empirical study for assessing how user characteristics,
temporal features, and the short/long-term contexts contribute to gains in
predicting users' app dwell time on the population level. The comprehensive
analysis is conducted on large app usage logs collected through a mobile
advertising company. The dataset covers more than 12K anonymous users and 1.3
million log events. Based on the analysis, we further investigate a novel
mobile app engagement prediction problem -- can we predict simultaneously what
app the user will use next and how long he/she will stay on that app? We
propose several strategies for this joint prediction problem and demonstrate
that our model can improve the performance significantly when compared with the
state-of-the-art baselines. Our work can help mobile system developers in
designing a better and more engagement-aware mobile app user experience.
