  Explainable recommendation is far from being well solved partly due to three
challenges. The first is the personalization of preference learning, which
requires that different items/users have different contributions to the
learning of user preference or item quality. The second one is dynamic
explanation, which is crucial for the timeliness of recommendation
explanations. The last one is the granularity of explanations. In practice,
aspect-level explanations are more persuasive than item-level or user-level
ones. In this paper, to address these challenges simultaneously, we propose a
novel model called Hybrid Deep Embedding (HDE) for aspect-based explainable
recommendations, which can make recommendations with dynamic aspect-level
explanations. The main idea of HDE is to learn the dynamic embeddings of users
and items for rating prediction and the dynamic latent aspect
preference/quality vectors for the generation of aspect-level explanations,
through fusion of the dynamic implicit feedbacks extracted from reviews and the
attentive user-item interactions. Particularly, as the aspect
preference/quality of users/items is learned automatically, HDE is able to
capture the impact of aspects that are not mentioned in reviews of a user or an
item. The extensive experiments conducted on real datasets verify the
recommending performance and explainability of HDE. The source code of our work
is available at \url{https://github.com/lola63/HDE-Python}
