no code implementations • 19 Jun 2023 • Mingshi Yan, Zhiyong Cheng, Jing Sun, Fuming Sun, Yuxin Peng
In this paper, we propose MB-HGCN, a novel multi-behavior recommendation model that uses a hierarchical graph convolutional network to learn user and item embeddings from coarse-grained on the global level to fine-grained on the behavior-specific level.
1 code implementation • 26 May 2022 • Mingshi Yan, Zhiyong Cheng, Chen Gao, Jing Sun, Fan Liu, Fuming Sun, Haojie Li
In particular, we design a cascading residual graph convolutional network structure, which enables our model to learn user preferences by continuously refining user embeddings across different types of behaviors.