TIRAGNN: Temporal and Implicit Relation-Aware Graph Neural Networks for Social Recommendation

Social recommendation systems predict user preferences by using social relationships to address data sparsity and cold-start problems. Since social relations and user–item interactions can naturally be modeled as graph structures, graph neural networks (GNNs) have achieved significant success in social recommendation. However, most existing models fail to incorporate temporal information when modeling user–item interactions and rely solely on explicit social relationships to capture user influence, resulting in suboptimal performance. To address these problems, this article presents a temporal and implicit relation-aware graph neural network for social recommendation (TIRAGNN). Specifically, we model user and item representations using rating and temporal information from user–item interactions, integrating their relational influence within both the social graph and the constructed auxiliary graphs. Additionally, attention mechanisms are employed to model interaction sequences and aggregate relational influences, thereby enhancing the learning of user and item representations. Experimental results on two real-world datasets verify the superiority of TIRAGNN over state-of-the-art approaches.