Degree-aware embedding-based multi-correlated graph convolutional collaborative filtering

In light of the remarkable capacity of graph convolutional network (GCN) in representation learning, researchers have incorporated it into collaborative filtering recommendation systems to capture high-order collaborative signals. However, existing GCN-based collaborative filtering models still exhibit three deficiencies: the failure to consider differences between users’ activity and preferences for items’ popularity, the low-order feature information of users and items has been inadequately employed, and neglecting the correlated relationships among isomorphic nodes. To address these shortcomings, this paper proposes a degree-aware embedding-based multi-correlated graph convolutional collaborative filtering (Da-MCGCF). Firstly, Da-MCGCF combines users’ activity and preferences for items’ popularity to perform neighborhood aggregation in the user-item bipartite graph, thereby generating more precise representations of users and items. Secondly, Da-MCGCF employs a low-order feature fusion strategy to integrate low-order features into the process of mining high-order features, which enhances feature representation capabilities, and enables the exploration of deeper relationships. Furthermore, we construct two isomorphic graphs by employing an adaptive approach to explore correlated relationships at the isomorphic level between users and items. Subsequently, we aggregate the features of isomorphic users and items separately to complement their representations. Finally, we conducted extensive experiments on four public datasets, thereby validating the effectiveness of our proposed model.