Personalized recommender systems are increasingly important as more content and services become available and users struggle to identify what might interest them. Thanks to the ability for providing rich information, knowledge graphs (KGs) are being incorporated to enhance the recommendation performance and interpretability. To effectively make use of the knowledge graph, we propose a recommendation model in the hyperbolic space, which facilitates the learning of the hierarchical structure of knowledge graphs. Furthermore, a hyperbolic attention network is employed to determine the relative importances of neighboring entities of a certain item. In addition, we propose an adaptive and fine-grained regularization mechanism to adaptively regularize items and their neighboring representations. Via a comparison using three real-world datasets with state-of-the-art methods, we show that the proposed model outperforms the best existing models by 2-16% in terms of NDCG@K on Top-K recommendation.

翻译：随着更多的内容和服务能够提供,用户在努力确定它们可能感兴趣的内容和服务时,个人化推荐系统越来越重要。由于能够提供丰富的信息,知识图(KGs)正在被纳入,以提高建议性能和可解释性。为了有效利用知识图,我们提议在双曲空间建立一个建议模型,便于学习知识图的等级结构。此外,还使用双曲关注网络来确定某一项目的相邻实体的相对重要性。此外,我们提议建立一个适应性和精细的正规化机制,以适应性地规范项目及其相邻的表示方式。我们用三种真实世界数据集与最新方法进行比较,我们显示,拟议的模型比现有最佳模型高出2-16%,在Top-K建议上是NDCG@K。