  Currently, there starts a research trend to leverage neural architecture for
recommendation systems. Though several deep recommender models are proposed,
most methods are too simple to characterize users' complex preference. In this
paper, for a fine-grain analysis, users' ratings are explained from multiple
perspectives, based on which, we propose our neural architecture. Specifically,
our model employs several sequential stages to encode the user and item into
hidden representations. In one stage, the user and item are represented from
multiple perspectives and in each perspective, the representations of user and
item put attentions to each other. Last, we metric the output representations
of final stage to approach the users' rating. Extensive experiments demonstrate
that our method achieves substantial improvements against baselines.
