Multi-Perspective Neural Architecture for Recommendation System
This work addresses the need for more sophisticated recommendation systems for users, though it appears incremental as it builds on existing neural methods.
The paper tackles the problem of modeling users' complex preferences in recommendation systems by proposing a multi-perspective neural architecture that encodes user and item representations with attention mechanisms, achieving substantial improvements over baselines in experiments.
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.