Multi-Perspective Neural Architecture for Recommendation System

TL;DR

This paper introduces a multi-perspective neural architecture for recommendation systems that captures complex user preferences through multiple representation stages, leading to improved prediction accuracy.

Contribution

It proposes a novel neural model that encodes users and items from multiple perspectives with attention mechanisms, enhancing recommendation performance.

Findings

Achieves significant improvements over baseline models.

Effectively captures complex user preferences.

Demonstrates robustness across various datasets.

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-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.