Dual Graph Neural Networks for Dynamic Users’ Behavior Prediction on Social Networking Services

Social network services (SNSs) provide platforms where users engage in social link behavior (e.g., predicting social relationships) and consumption behavior. Recent advancements in deep learning for recommendation and link prediction explore the symbiotic relationships between these behaviors, leveraging social influence theory and user homogeneity, i.e., users tend to accept recommendations from social friends and connect with like-minded users. These studies yield positive feedback for users and platforms, fostering practical applications and economic development. While previous works jointly model these behaviors, most studies often overlook the evolution of social relationships and users’ preferences in dynamic scenes and the correlations inside, as well as the higher order information within the social network and preference network (consumption history). To address this, we propose the dynamic graph neural joint behavior prediction model (DGN-JBP). Specifically, we actively disentangle and initialize user embeddings from multiple perspectives to refine information for modeling. Additionally, we design an attentive graph neural network and combine it with gate recurrent units (GRUs) to extract high-order dynamic information. Finally, we design a dual framework and purposefully fuse embeddings to mutually enhance the effectiveness of predictions on two prediction tasks. Extensive experimental results on two real-world datasets clearly demonstrate the effectiveness of our proposed model.