A Learning-Based POI Recommendation with Spatiotemporal Context Awareness

Point-of-Interest (POI) recommendation systems are integral to enhancing user experiences in location-based services.Traditional systems often overlook the influence of contextual factors such as time and location, resulting in suboptimal recommendations.This paper introduces a learning-based POI recommendation model that incorporates spatiotemporal context awareness to provide more accurate and personalized suggestions.By leveraging users' past check-ins, current location, and temporal patterns, the proposed system can effectively capture the dynamics of user behavior.The model integrates deep learning techniques to process heterogeneous data and learn high-level user representations.Experimental evaluations using real-world datasets demonstrate that the proposed system significantly outperforms baseline models in terms of recommendation precision, recall, and user satisfaction, proving the importance of spatiotemporal context in next-POI recommendation tasks.