PNMLR: Enhancing Route Recommendations With Personalized Preferences Using Graph Attention Networks

In the modern landscape, millions of users rely on mapping services daily to plan trips of various kinds. While these services traditionally focus on providing the shortest routes, individual preferences often influence real-world route choices. However, most existing solutions neglect the impact of such preferences, limiting their ability to deliver truly personalized recommendations. This paper presents the Personalized Neuro-MLR (PNMLR) model, which enhances route recommendations by embedding user-specific preferences into the prediction process. Built on the Neuro-MLR (NMLR) framework, PNMLR leverages Graph Attention Networks (GAT) to integrate factors like user ID, time of day, and transport mode. This approach captures the variability in user behavior, offering personalized predictions for the most likely route. Extensive experiments on the Geolife GPS dataset show significant improvements across key metrics, including F1-score, precision, recall and route reachability, compared to models that do not consider user preferences. These results demonstrate the potential of PNMLR to transform route recommendation systems from generic solutions to user-centric models.