Recommendation Model Based on Higher-Order Semantics and Node Attention in Heterogeneous Graph Neural Networks - Siyue Li; Tian Jin; Hao Luo; Erfan Wang; Ranting Tao (Mathematics)
In recent years, graph neural networks (GNNs) have been widely applied in recommendation systems. However, most existing GNN models do not fully consider the complex relationships between heterogeneous nodes and ignore the high-order semantic information in the interactions between different types of nodes, which limits the recommendation performance. To address these issues, this paper proposes a heterogeneous graph neural network recommendation model based on high-order semantics and node attention (HAS-HGNN). Firstly, HAS-HGNN aggregates the features of direct neighboring nodes through an interest aggregation layer to capture the information of items that users are interested in. This method of capturing the features of directly interacting nodes can effectively uncover users’ potential interests. Meanwhile, considering that users with multiple interactions may share similar interests, in the common interest feature capture layer, HAS-HGNN utilizes semantic relationships to capture the features of users with the same interests, generating common interest features among users with multiple interactions. Finally, HAS-HGNN combines the direct features of users with the interest features between other users through a feature fusion layer to generate the final feature representation. Experimental results show that the proposed model significantly outperforms existing baseline methods on multiple real-world datasets, providing new insights and methods for the application of heterogeneous graph neural networks in recommendation systems.