  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.
