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Recommender systems often use latent features to explain the behaviors ofusers and capture the properties of items. As users interact with differentitems over time, user and item features can influence each other, evolve andco-evolve over time. The compatibility of user and item's feature furtherinfluence the future interaction between users and items. Recently, pointprocess based models have been proposed in the literature aiming to capture thetemporally evolving nature of these latent features. However, these modelsoften make strong parametric assumptions about the evolution process of theuser and item latent features, which may not reflect the reality, and haslimited power in expressing the complex and nonlinear dynamics underlying theseprocesses. To address these limitations, we propose a novel deep coevolutionarynetwork model (DeepCoevolve), for learning user and item features based ontheir interaction graph. DeepCoevolve use recurrent neural network (RNN) overevolving networks to define the intensity function in point processes, whichallows the model to capture complex mutual influence between users and items,and the feature evolution over time. We also develop an efficient procedure fortraining the model parameters, and show that the learned models lead tosignificant improvements in recommendation and activity prediction compared toprevious state-of-the-arts parametric models.

Deep Coevolutionary Network: Embedding User and Item Features for Recommendation