  Users install many apps on their smartphones, raising issues related to
information overload for users and resource management for devices. Moreover,
the recent increase in the use of personal assistants has made mobile devices
even more pervasive in users' lives. This paper addresses two research problems
that are vital for developing effective personal mobile assistants: target apps
selection and recommendation. The former is the key component of a unified
mobile search system: a system that addresses the users' information needs for
all the apps installed on their devices with a unified mode of access. The
latter, instead, predicts the next apps that the users would want to launch.
Here we focus on context-aware models to leverage the rich contextual
information available to mobile devices. We design an in situ study to collect
thousands of mobile queries enriched with mobile sensor data (now publicly
available for research purposes). With the aid of this dataset, we study the
user behavior in the context of these tasks and propose a family of
context-aware neural models that take into account the sequential, temporal,
and personal behavior of users. We study several state-of-the-art models and
show that the proposed models significantly outperform the baselines.
