<![CDATA[Background: As government e-services expand, the need to offer personalized services to each citizen is becoming increasingly important. However, government systems face limitations in utilizing user and service-specific features for model training, as training data is typically restricted to historical service usage records. This constraint poses a significant challenge in delivering practical, personalized recommendations. Objective: This study aims to demonstrate the feasibility of detecting latent collaborative filtering signals in government e-service usage data using a GNN-based approach, and to evaluate how effective graph neural network-based recommendation methods are at identifying these signals using only historical interaction records. Methods: Accordingly, we explore the application of LightGCN to model user-service interactions based solely on historical behavioral data. In this study, we constructed a bipartite graph from real-world usage data and trained a model to uncover latent patterns in user preferences. Results: Through hyperparameter tuning, our experiments achieved the following performance metrics: Recall@20 = 0.175, Precision@20 = 0.068, and NDCG@20 = 0.155. Conclusion: These results support our hypothesis, demonstrating that the graph neural network-based model can capture latent collaborative signals even under sparse data conditions. Consequently, LightGCN presents a promising approach for generating personalized recommendations in the context of government e-services. Keywords: Collaborative Filtering, E-service, GNN-based Recommendation System, LightGCN, Recommendation System]]>
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