  Modeling tap or click sequences of users on a mobile device can improve our
understandings of interaction behavior and offers opportunities for UI
optimization by recommending next element the user might want to click on. We
analyzed a large-scale dataset of over 20 million clicks from more than 4,000
mobile users who opted in. We then designed a deep learning model that predicts
the next element that the user clicks given the user's click history, the
structural information of the UI screen, and the current context such as the
time of the day. We thoroughly investigated the deep model by comparing it with
a set of baseline methods based on the dataset. The experiments show that our
model achieves 48% and 71% accuracy (top-1 and top-3) for predicting next
clicks based on a held-out dataset of test users, which significantly
outperformed all the baseline methods with a large margin. We discussed a few
scenarios for integrating the model in mobile interaction and how users can
potentially benefit from the model.
