Knowledge Based Hyperbolic Propagation
Chang-You Tai, Chien-Kun Huang, Liang-Ying Huang, Lun‐Wei Ku · 2021
There has been significant progress in utilizing heterogeneous knowledge graphs (KGs) as auxiliary information in recommendation systems. However, existing KG-aware recommendation models rely solely on Euclidean space, neglecting hyperbolic space, which has already been shown to possess a superior ability to separate embed-dings by providing more "room". We propose a knowledge-based hyperbolic propagation framework (KBHP) which includes hyperbolic components for calculating the importance of KG attributes relative to achieve better knowledge propagation. In addition to the original relations in the knowledge graph, we propose a user purchase relation to better represent logical patterns in hyperbolic space, which bridges users and items for modeling user preference. Experiments on four real-world benchmarks show that KBHP is significantly more accurate than state-of-the-art models. We further visualize the generated embeddings to demonstrate that the proposed model successfully clusters attributes that are relevant to items and highlights those that contain useful information for the recommendation.