ARTAN: Align reviews with topics in attention network for rating prediction

Abstract

Rating prediction has long been a popular research topic in the field of recommendation systems. Latent factor models, particularly matrix factorization (MF) methods, constitute the most prevalent techniques for rating prediction. However, MF-based methods suffer from the problems of data sparsity and lack of explicability. In this paper, we propose a novel model in which ratings and topic-level review information are integrated into a deep neural framework to address these problems. Specifically, we designed a topic alignment operation and a topic attention mechanism to reflect the user’s preferences and an item’s properties in terms of a topic in the reviews. We present a neural prediction layer that extends user and item representations by including both the latent factors from ratings and the textual information from reviews. The results of extensive experiments on four real-world datasets demonstrate that our proposed method consistently outperforms the state-of-the-art recommendation approaches that follow this direction in terms of rating prediction. Furthermore, our model can categorize representative reviews of users/items and group reviews into topics for the users’ reference.