  This paper utilizes well-designed item-item path modelling between
consecutive items with attention mechanisms to sequentially model dynamic
user-item evolutions on dynamic knowledge graph for explainable
recommendations. Compared with existing works that use heavy recurrent neural
networks to model temporal information, we propose simple but effective neural
networks to capture user historical item features and path-based context to
characterise next purchased item. Extensive evaluations of TMER on three
real-world benchmark datasets show state-of-the-art performance compared
against recent strong baselines.
