  Explainable recommendation systems provide explanations for recommendation
results to improve their transparency and persuasiveness. The existing
explainable recommendation methods generate textual explanations without
explicitly considering the user's preferences on different aspects of the item.
In this paper, we propose a novel explanation generation framework, named
Hierarchical Aspect-guided explanation Generation (HAG), for explainable
recommendation. Specifically, HAG employs a review-based syntax graph to
provide a unified view of the user/item details. An aspect-guided graph pooling
operator is proposed to extract the aspect-relevant information from the
review-based syntax graphs to model the user's preferences on an item at the
aspect level. Then, a hierarchical explanation decoder is developed to generate
aspects and aspect-relevant explanations based on the attention mechanism. The
experimental results on three real datasets indicate that HAG outperforms
state-of-the-art explanation generation methods in both single-aspect and
multi-aspect explanation generation tasks, and also achieves comparable or even
better preference prediction accuracy than strong baseline methods.
