Temporal knowledge graph recommendation with sequence-aware and path reasoning

Knowledge graph recommendation (KGRec) models not only alleviate the issues of data sparsity and the cold start problem encountered by traditional models but also enhance interpretability and credibility through the provision of explicit recommendation rationales. Nonetheless, existing KGRec models predominantly concentrate on extracting static structural features of user preferences from KG, often neglecting the dynamic temporal features, such as purchase time and click time. This oversight results in considerable limitations in recommendation performance. In response to this challenge, this paper introduces a novel temporal knowledge graph recommendation model (TKGRec), which fully utilizes both dynamic temporal feature and static structure feature for better recommendation. We specifically construct a temporal KG that encapsulates both static and dynamic user–item interactions. Based on the new environment, we propose a sequence-aware and path reasoning framework, in which the sequence-aware module employs a dual-attention mechanism to distill temporal features from interactions, whereas the path reasoning module utilizes reinforcement learning to extract path features. These two modules are seamlessly fused and iteratively refined to capture a more holistic understanding of user preferences. Experimental results on three real-world datasets demonstrate that the proposed model significantly outperforms existing state-of-the-art baseline models in terms of performance.