Light dual hypergraph convolution for collaborative filtering

Meng Jian, Langchen Lang, Jingjing Guo, Zun Li, Tuo Wang, Lifang Wu

Abstract: Highlights•Homogeneous user/item correlations bring entangled CF signals beyond the single CF signal in heterogeneous user-item interactions, which enables hybrid learning with much more reliable CF signals to alleviate interaction sparsity and confront long-tail bias.•This work proposes a light dual hypergraph convolution (LDHC) model by hybrid learning with a hypergraph convolution to predict interests, which performs a two-level interest propagation within the heterogeneous correlations and between the homogeneous ones.•A light convolution is introduced in hypergraph-based interest propagation to lighten the LDHC model for reducing the burden and difficulty in training.