Knowledge-Enhanced Top-K Recommendation in Poincaré Ball
This work addresses the challenge of personalized content discovery for users by improving recommendation accuracy through knowledge graph integration, though it is incremental as it builds on existing hyperbolic and attention-based approaches.
The paper tackles the problem of enhancing top-K recommendation by incorporating knowledge graphs into hyperbolic space to capture hierarchical structures, resulting in a 2-16% improvement in NDCG@K over state-of-the-art methods on three real-world datasets.
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.