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.

翻译：在网上服务中,用户的周遭是威胁服务健康和盈利的重要问题。以前关于周遭预测的大部分工作将问题转换成二进制分类任务,将用户标为churned和非curned。最近,一些工作试图将用户的周遭预测问题转换成用户返回时间的预测。在这个方法中,在现实世界在线服务中更为现实,模型的每个时间步骤都预测用户返回时间,而不是预测一个周遭标签。然而,这一类别的以往工作缺乏普遍性,需要很高的计算复杂性。在本文件中,我们引入了“emph{Choracle}”这一标志,通过利用时空点进程和常规神经网络的组合,预测用户返回服务的时间。此外,我们把潜在变量纳入拟议的经常性神经网络,以模拟潜在用户对系统的忠诚度。我们还开发了一种高效的近似变异算算法,以便利用回传时间来学习拟议的网络数据库的参数。最后,我们展示了世界真实数据种类的优异性。