Sentiment-aware deep recommender system with neural attention networks

Abstract

With the advent of web technology, user-generated textual reviews are becoming increasingly accumulated on many e-commerce websites. These reviews contain not only the user comments on different aspects of the products but also the user sentiments associated with the aspects. Although these user sentiments serve as vital side information for improving the performance of recommender systems, most existing approaches ignore to fully exploit them in modeling the fine-grained user-item interaction for improving recommender system performance. Thus, this paper proposes a sentiment-aware deep recommender system with neural attention network (SDRA), which can capture both the aspects of products and the underlying user sentiments associated with the aspects for improving the recommendation system performance. Particularly, a semi-supervised topic model is designed to extract the aspects of the product and the associated sentiment lexicons from the user textual reviews, which are then incorporated into a long short term memory (LSTM) encoder via an interactive neural attention mechanism for better learning of the user and item sentiment-aware representation. Furthermore, a co-attention mechanism is introduced to better model the fine-grained user-item interaction for improving predictive performance. The extensive experiments on different datasets showed that our proposed SDRA model can achieve better performance over the baseline approaches.