Electric vehicle charging station recommendation system based on graph neural network and context-aware refinement

As interest in eco-friendly transportation increases, the demand for electric vehicles (EVs) is also growing. With this trend, establishing an efficient charging infrastructure in urban environments has become essential. However, despite the presence of charging station infrastructure, its utilization remains low. In this study, we introduce a context-aware refining approach that incorporates location proximity and charging type preference into a Graph Neural Network (GNN)-based recommendation algorithm. For the EV charging station data, we collect and manage the large-scale real-time data. Additionally, to overcome the limitations posed by the absence or restricted access to driver profile data–an essential component for developing a recommendation system–we develop a simulator that analyzes and replicates real drivers’ charging patterns and behavioral characteristics. This enables us to generate realistic and reliable driver profile data. We employ the state-of-the-art GNN-based recommendation model for the base collaborative filtering. Then, we incorporate the user’s location proximity to the charging station and charging type preference (i.e., slow/fast) as context-aware refinement factors for the final recommendation results. To further enhance recommendation performance, we go beyond simple location proximity utilization by applying a clustering-based approach that reflects the actual spatial distribution, considering both charging stations and users. Through extensive experiments, we demonstrate that incorporating our contextual refinements consistently improves recommendation quality compared to the baseline GNN approach across different collaborative filtering models. In particular, leveraging charging type information and spatial clustering leads to substantial and stable performance gains, and their combined use yields the most robust results. These findings highlight the importance of jointly modeling functional preferences and geographical context for effective EV charging station recommendations.