Journal of Information Systems Engineering and Business Intelligence | 2026
Authors: Gantulga S.; Ganbold A.
DOI: 10.20473/jisebi.12.1.182-195
Journal: Journal of Information Systems Engineering and Business Intelligence
Year: 2026
Publisher: Airlangga University
Document Type: Article
Open Access: All Open Access; Gold Open Access
Cited by: 0
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. © 2026 The Authors. Published by Universitas Airlangga.
Collaborative Filtering; E-service; GNN-based Recommendation System; LightGCN; Recommendation System