  In both mobile and web applications, speeding up user interface response
times can often lead to significant improvements in user engagement. A common
technique to improve responsiveness is to precompute data ahead of time for
specific activities. However, simply precomputing data for all user and
activity combinations is prohibitive at scale due to both network constraints
and server-side computational costs. It is therefore important to accurately
predict per-user application usage in order to minimize wasted precomputation
("predictive precompute"). In this paper, we describe the novel application of
recurrent neural networks (RNNs) for predictive precompute. We compare their
performance with traditional machine learning models, and share findings from
their large-scale production use at Facebook. We demonstrate that RNN models
improve prediction accuracy, eliminate most feature engineering steps, and
reduce the computational cost of serving predictions by an order of magnitude.
