Social Media User Behavior Modeling and Content Distribution Optimization Using GAT and Temporal Graph Networks
Shiyu Yang · 2025
To address the challenges of effectively capturing the dynamic nature of user behavior and complex interactions in social media content distribution, this paper proposes a joint modeling approach that integrates a graph attention network (GAT) and a temporal graph network (TGN). Current static graph-based approaches suffer from two major flaws: ignoring the temporal evolution of user interests and failing to handle heterogeneous node relationships, which limits recommendation accuracy and user dwell time. This study first constructs a heterogeneous user-content graph using the GAT and employs a multi-head attention mechanism (8 heads) to quantify the interaction weights between users, posts, and topic nodes. Secondly, a temporal graph network module is developed, employing a temporal encoder and a gated recurrent unit (GRU) to model the dynamic features of user behavior sequences over a 28-day time window. Furthermore, a dynamic negative sampling strategy is proposed to enhance the model's ability to recognize implicit feedback by adaptively adjusting the negative sample ratio (1:3 to 1:5). Finally, a multi-task learning framework is constructed to jointly optimize click-through rate (CTR) and dwell time metrics. A/B testing on Twitter and Weibo datasets demonstrates that the proposed model maintains a stable daily CTR of 10.35%-11%, with a peak average dwell time of 31.8 seconds. Experiments have shown that this method can effectively capture the spatiotemporal evolution of user behavior, providing a new technical approach for personalized recommendations and content resource allocation on social media platforms.