Hierarchical Attention based Neural Network for Explainable Recommendation

In recent years, recommendation systems have attracted more and more attention due to the rapid development of e-commerce. Reviews information can offer help in modeling user's preference and item's performance. Some existing methods utilize reviews for the recommendation. However, few of those models consider the importance of reviews and words in corpus together. Therefore, we propose an approach for rating prediction using a hierarchical attention-based network named HANN, which can distinguish the importance of reviews at both word level and review level for explanations automatically. Experiments on four real-life datasets from Amazon demonstrate that our model achieves an improvement in prediction compared to several state-of-the-art approaches. The hierarchical attention weights in sampled test data verify the effect on selecting informative words and reviews.