MSRHNN:Multidimensional Social Relation under Heterogeneous Neural Network for Recommendation

Abstract With the growing popularity of mobile smart devices and the availability of 4G and 5G networks, social recommendation systems have become a hot research topic for industrial applications. Social networks among users are built through social information, which can improve collaborative filtering and solve cold start and sparsity problems. Graph convolutional networks have been widely used to represent the interactions and structural relationships among entities, but the exponential increase of the domain introduces a new problem. In this paper, we propose a multidimensional social relation under heterogeneous neural network (MSRHNN). By embedding historical evaluations, various social networks constituting different dimensions, the attention integration of different social networks on user preferences is achieved. A static graph encoder is used to process the node representation of each heterogeneous graph at different time steps, which includes the node attribute information and edge information, to capture the changing feature information of the nodes in all time steps of the dynamic graph, and to obtain an effective node representation at each time step. The goal of the heterogeneous dynamic graph neural network is to capture the changing node representations of the same nodes and different nodes in the graph network at different time steps, in order to better perform the task of node classification in dynamic graphs. In this paper, the model of heterogeneous dynamic graph neural network is verified from data, and experiments show that heterogeneous dynamic graph neural network outperforms other representation learning methods.