Simplices-based higher-order enhancement graph neural network for multi-behavior recommendation

Multi-behavior recommendations effectively integrate various types of behaviors and have been proven to enhance recommendation performance. However, existing researches primarily focus on distinguishing between various behaviors, neglecting the exploration of common representations within each behavior that might reflect individual preferences from different perspectives. Meanwhile, interactions within each behavior remain sparse; how to learn effective information from limited data poses a significant challenge. In this study, we propose a simplices-based higher-order enhancement graph neural network for multi-behavior recommendations, HEM-GNN. Specifically, we adopt a supervised method to distinguish the importance of different behaviors and perform inter-behavior representation learning. Meanwhile, for each behavior, we define implicit relationships to mitigate data sparsity, and then aggregate information from nodes within simplices to extract their higher-order commonalities. Finally, HEM-GNN leverages these representations to make recommendations. Through experiments on three public datasets (Taobao, Beibei, and IJCAI), HEM-GNN demonstrates better performance compared to 10 baseline algorithms. It outperforms state-of-the-art models by margins ranging from 8.99% to 10.58% in HR@$K$ and 8.18% to 9.69% in NDCG@$K$, highlighting the significance of higher-order features in multi-behavior recommendations. The model and datasets are released at: https://github.com/SamuelZack/MultiRec.