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.
@article{arxiv.2101.01433,
title = {Temporal Meta-path Guided Explainable Recommendation},
author = {Hongxu Chen and Yicong Li and Xiangguo Sun and Guandong Xu and Hongzhi Yin},
journal= {arXiv preprint arXiv:2101.01433},
year = {2021}
}