Graph Neural Network Session Recommendation Model with Fusion Modeling

To address the limitations of traditional session recommendation algorithms that rely solely on explicit information while overlooking potential interactions between sessions, this paper proposes a novel integrated modeling approach based on gating mechanisms and graph attention networks—IM-GGN (Integrated Modeling Gated Graph Network). This model simultaneously captures structured relationships between items and unstructured associations across sessions to enhance recommendation performance. Specifically, the model comprises two main components: the Structured Pattern Learning (SPL) module and the Unstructured Pattern Learning (UPL) module. The SPL module integrates graph neural networks with gating mechanisms to model sequential dependencies and long-range relationships within sessions. Meanwhile, the UPL module leverages graph attention mechanisms to capture unstructured inter-session correlations, thereby extracting contextual user preferences. Experimental results on multiple public datasets demonstrate that the proposed method achieves notable performance improvements, confirming its effectiveness in session-based recommendation tasks.