Abstract: Systems, apparatuses, and methods for leveraging temporal user behavior, such as web browsing history or social media interactions, to “predict” the occurrence and/or timing of one or more subsequent events. A multi-task architecture models temporal user-behavior with respect to single or multiple objectives and optimizes the neural network architecture and task-grouping to achieve the most accurate predictions. The deep attention-based uni-directional or bi-directional recurrent neural network (RNN) models that are part of the disclosed architecture can be used directly as an end-to-end prediction or inference system or can be used to generate learned representations of temporal data which can be extracted and used in a separate model or architecture.