Decoupled Hyperbolic Graph Attention Network for Modeling Substitutable and Complementary Item Relationships

Zhiheng Zhou, Tao Wang, Linfang Hou, Xinyuan Zhou, Mian Ma, Zhuoye Ding

Abstract: Modeling substitutable and complementary item relationships is a fundamental and important topic for recommendation in e-commerce online scenarios. In the real world, item relationships are usually coupled, heterogeneous and they also have abundant side information and hierarchical data structures. Recently, to take full advantage of both sides information and topological structure, graph neural networks are widely explored in relationship modeling. However, the existing methods are crude in decoupling heterogeneous relationships. Their model designs lack deep insight of relationships' coupling mode, i.e. neglects the prior knowledge of how relationships affect each other. In addition, many existing graph methods, regardless of how they handle coupled relationships, are deployed in Euclidean spaces, which distorts hierarchical data structure and limits the expressive power due to the non power law characteristic of Euclidean topology. In this paper, we propose a novel Decoupled Hyperbolic Graph Attention Network (DHGAN). The innovations of our DHGAN can be highlighted as two aspects. Firstly, we design metapaths in an adequate way following an algebraic perspective of relationships coupling mode, which helps achieving better model interpretability. Secondly, DHGAN maps heterogeneous relationships into separate hyperbolic spaces, which can better capture the hierarchical information of graph nodes and helps improving model's representational capacity. We conduct extensive experiments on three public real-world datasets, demonstrating DHGAN is superior to the state-of-the-art graph baselines. We release the codes at https://github.com/wt-tju/DHGAN.