  Recommender systems often use latent features to explain the behaviors of
users and capture the properties of items. As users interact with different
items over time, user and item features can influence each other, evolve and
co-evolve over time. The compatibility of user and item's feature further
influence the future interaction between users and items. Recently, point
process based models have been proposed in the literature aiming to capture the
temporally evolving nature of these latent features. However, these models
often make strong parametric assumptions about the evolution process of the
user and item latent features, which may not reflect the reality, and has
limited power in expressing the complex and nonlinear dynamics underlying these
processes. To address these limitations, we propose a novel deep coevolutionary
network model (DeepCoevolve), for learning user and item features based on
their interaction graph. DeepCoevolve use recurrent neural network (RNN) over
evolving networks to define the intensity function in point processes, which
allows the model to capture complex mutual influence between users and items,
and the feature evolution over time. We also develop an efficient procedure for
training the model parameters, and show that the learned models lead to
significant improvements in recommendation and activity prediction compared to
previous state-of-the-arts parametric models.
