Session-based recommendation systems often struggle to personalise recommendations because they focus on item sequences within a user's current session, overlooking valuable historical data and broader item relationships. While Heterogeneous Global Graph Neural Networks (HG-GNNs) can capture long-term and short-term user preferences, their complexity can be prohibitive. To address this, we propose a method that forms clusters of users based on their implicit preferences, reducing the complexity while still effectively modelling user preferences. By clustering users with similar implicit preferences, our method simplifies the recommendation process while maintaining the ability to capture diverse preferences. This clustering approach allows for more efficient learning within the GNN, balancing the need for accuracy with computational feasibility. Experiments on real-world datasets show that our approach maintains comparable accuracy to traditional HG-GNN methods while significantly reducing computational overhead. This balance of efficiency and effectiveness sets a new standard for session-based recommendation systems.