Fully Hyperbolic Graph Convolution Network for Recommendation
This work addresses the limitation of Euclidean embeddings in modeling tree-like user-item relationships for recommendation systems, though it appears incremental as it adapts existing hyperbolic approaches to a specific domain.
The authors tackled the problem of capturing hierarchical structures in recommendation systems by proposing a fully hyperbolic graph convolution network, which outperformed both Euclidean and hyperbolic baselines while requiring significantly lower embedding dimensionality.
Recently, Graph Convolution Network (GCN) based methods have achieved outstanding performance for recommendation. These methods embed users and items in Euclidean space, and perform graph convolution on user-item interaction graphs. However, real-world datasets usually exhibit tree-like hierarchical structures, which make Euclidean space less effective in capturing user-item relationship. In contrast, hyperbolic space, as a continuous analogue of a tree-graph, provides a promising alternative. In this paper, we propose a fully hyperbolic GCN model for recommendation, where all operations are performed in hyperbolic space. Utilizing the advantage of hyperbolic space, our method is able to embed users/items with less distortion and capture user-item interaction relationship more accurately. Extensive experiments on public benchmark datasets show that our method outperforms both Euclidean and hyperbolic counterparts and requires far lower embedding dimensionality to achieve comparable performance.