Geometry Interaction Augmented Graph Collaborative Filtering

Jie Xu, Chaozhuo Li

Abstract: Graph collaborative filtering, which could capture the abundant collaborative signal from the high-order connectivity of the tree-likeness user-item interaction graph, has received considerable research attention recently. Most graph collaborative filtering methods embed graphs in the Euclidean spaces, but that could have high distortion when embedding graphs with tree-likeness structure. Recently, some researchers address this problem by learning the feature representations in the hyperbolic spaces. However, because the user-item interaction graphs also have cyclic structure, the high-order collaborative signal cannot be well captured by hyperbolic spaces. From this point of view, neither Euclidean spaces nor hyperbolic spaces can capture the full information from the complexity of user-item interactions. Therefore, how to construct a suitable embedding space for graph collaboration filtering is an important problem. In this paper, we analyze the properties of hyperbolic geometry in graph collaborative filtering tasks and proposed a novel geometry interaction augmented graph collaborative filtering (GeoGCF) method, which leverages both Euclidean and hyperbolic geometry to model the user-item interactions. Experimental results show the effectiveness of the proposed method.