Align Reviews with Topics in Attention Network for Rating Prediction

Abstract

Rating prediction has long been a hot research topic in recommendation systems. Latent factor models, in particular, matrix factorization (MF), are the most prevalent techniques for rating prediction. However, MF based methods suffer from the problem of data sparsity and lack of explanation. In this paper, we present a novel model to address these problems by integrating ratings and topic-level review information into a deep neural framework. Our model can capture the varying attentions that a review contributes to a user/item at the topic level. We conduct extensive experiments on three datasets from Amazon. Results demonstrate our proposed method consistently outperforms the state-of-the-art recommendation approaches.