Knowledge graphs contain rich semantic relationships related to items and incorporating such semantic relationships into recommender systems helps to explore the latent connections of items, thus improving the accuracy of prediction and enhancing the explainability of recommendations. However, such explainability is not adapted to users' contexts, which can significantly influence their preferences. In this work, we propose CA-KGCN (Context-Aware Knowledge Graph Convolutional Network), an end-to-end framework that can model users' preferences adapted to their contexts and can incorporate rich semantic relationships in the knowledge graph related to items. This framework captures users' attention to different factors: contexts and features of items. More specifically, the framework can model users' preferences adapted to their contexts and provide explanations adapted to the given context. Experiments on three real-world datasets show the effectiveness of our framework: modeling users' preferences adapted to their contexts and explaining the recommendations generated.

翻译：知识图谱包含与物品相关的丰富语义关系，将这些语义关系融入推荐系统有助于探索物品的潜在关联，从而提升预测准确性并增强推荐的可解释性。然而，此类可解释性并未根据用户上下文进行适配，而上下文可能显著影响用户偏好。本文提出CA-KGCN（上下文感知知识图谱卷积网络），这是一个端到端框架，能够根据用户上下文建模其偏好，并整合知识图谱中与物品相关的丰富语义关系。该框架可捕捉用户对不同因素的注意力：上下文及物品特征。具体而言，该框架能够根据用户上下文建模其偏好，并提供适配给定上下文的解释。在三个真实数据集上的实验表明了我们框架的有效性：根据用户上下文建模其偏好并解释生成的推荐结果。