Multi-perspective neural architecture for recommendation system.

Abstract

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-grained analysis, users' ratings are explained from multiple perspectives, based on which, we propose our neural architectures. 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 representation of user and that of item put attentions to each other. Last, we metric the output representations from the final stage to approach the users' ratings. Extensive experiments demonstrate that our method achieves substantial improvements against baselines.