  User churn is an important issue in online services that threatens the health
and profitability of services. Most of the previous works on churn prediction
convert the problem into a binary classification task where the users are
labeled as churned and non-churned. More recently, some works have tried to
convert the user churn prediction problem into the prediction of user return
time. In this approach which is more realistic in real world online services,
at each time-step the model predicts the user return time instead of predicting
a churn label. However, the previous works in this category suffer from lack of
generality and require high computational complexity. In this paper, we
introduce \emph{ChOracle}, an oracle that predicts the user churn by modeling
the user return times to service by utilizing a combination of Temporal Point
Processes and Recurrent Neural Networks. Moreover, we incorporate latent
variables into the proposed recurrent neural network to model the latent user
loyalty to the system. We also develop an efficient approximate variational
algorithm for learning parameters of the proposed RNN by using back propagation
through time. Finally, we demonstrate the superior performance of ChOracle on a
wide variety of real world datasets.
