  Recent advances in path-based explainable recommendation systems have
attracted increasing attention thanks to the rich information provided by
knowledge graphs. Most existing explainable recommendations only utilize static
knowledge graphs and ignore the dynamic user-item evolutions, leading to less
convincing and inaccurate explanations. Although there are some works that
realize that modelling user's temporal sequential behaviour could boost the
performance and explainability of the recommender systems, most of them either
only focus on modelling user's sequential interactions within a path or
independently and separately of the recommendation mechanism. In this paper, we
propose a novel Temporal Meta-path Guided Explainable Recommendation leveraging
Reinforcement Learning (TMER-RL), which utilizes reinforcement item-item path
modelling between consecutive items with attention mechanisms to sequentially
model dynamic user-item evolutions on dynamic knowledge graph for explainable
recommendation. Compared with existing works that use heavy recurrent neural
networks to model temporal information, we propose simple but effective neural
networks to capture users' historical item features and path-based context to
characterize the next purchased item. Extensive evaluations of TMER on two
real-world datasets show state-of-the-art performance compared against recent
strong baselines.
