Light dual hypergraph convolution for collaborative filtering

Abstract

Recommender systems filter information to meet users’ personalized interests actively. Existing graph-based models typically extract users’ interests from a heterogeneous interaction graph. They do not distinguish learning between users and items, ignoring the heterogeneous property. In addition, the interaction sparsity and long-tail bias issues still limit the recommendation performance significantly. Fortunately, hidden homogeneous correlations that have a considerable volume can entangle abundant CF signals. In this paper, we propose a light dual hypergraph convolution (LDHC) for collaborative filtering, which designs a hypergraph to involve heterogeneous and homogeneous correlations with more CF signals confronting the challenges. Over the integrated hypergraph, a two-level interest propagation is performed within the heterogeneous interaction graph and between the homogeneous user/item graphs to model users’ interests, where learning on users and items is distinguished and collaborated by the homogeneous propagation. Specifically, hypergraph convolution is lightened by removing unnecessary parameters to propagate users’ interests. Extensive experiments on publicly available datasets demonstrate that the proposed LDHC outperforms the state-of-the-art baselines.