Commerce Social Network Data Analysis and Product Recommendation Optimization Using Graph Neural Networks

With the development of social e-commerce platforms, the social relationships between users have put forward higher requirements for the accuracy and robustness of personalized recommendation systems. Existing recommendation methods generally have problems such as insufficient dynamic modeling of user interests and difficulty in effectively integrating multi-source social information, which limits the performance of recommendation systems in practical application scenarios. To address the above challenges, this paper proposes a social recommendation model based on multi-relation heterogeneous graph neural networks, which can simultaneously model user-product interaction behaviors and social relationships between users, integrate structural semantics and temporal dynamic information, and achieve a fine description of user preferences. The proposed method uses a relation-aware attention mechanism to perform weighted aggregation on multiple edge types, and introduces a time decay factor to capture the evolutionary characteristics of user interests. Systematic experiments on a simulated social e-commerce dataset show that the model significantly outperforms mainstream baseline methods in multiple indicators such as Precision, Recall, and NDCG, verifying the advantages of the proposed model in recommendation accuracy and ranking ability. This study expands the modeling depth of graph neural networks in heterogeneous social graphs, and provides a scalable and high-performance recommendation strategy for social e-commerce platforms, showing good adaptability in challenging scenarios such as user cold start and data sparsity.