A Time-aware Knowledge Graphs model for Explainable recommendation

The transparency of knowledge graphs has garnered significant attention in the field of explainable recommendations. Existing works have not taken into account the factor of timeliness in the modeling process, which hinders the accurate learning of user and item representations. To address this research gap, we propose a time-aware recommendation knowledge graph model. By incorporating time embedding into each step of the modeling process, we better model the recommendation paths and improve the effectiveness of recommendations. We conducted experiments on two datasets, demonstrating the superiority of our proposed model.