Deep learning based personalized tourism recommendation for behavior analysis and route optimization

Cultural heritage tourism faces growing challenges in providing personalized experiences while ensuring sustainable site management, necessitating advanced technological solutions. Existing recommendation systems often fail to capture the complex spatiotemporal dynamics and diverse preferences of tourists, limiting their effectiveness. To address this, we propose a novel deep learning framework featuring three domain-specific innovations: (1) Heritage-aware Graph Neural Networks (H-GNNs) that model cultural significance propagation and temporal visitation dynamics unique to heritage sites, (2) Cultural Spatiotemporal Transformers (C-STTs) with heritage-specific attention mechanisms that balance cultural value preservation with tourist preferences, and (3) Heritage-adaptive Transfer Reinforcement Learning (H-TRL) that incorporates cultural site constraints and preservation priorities into the reward structure. Experimental results demonstrate improvements, with the GNN module achieving a 14% increase in behavior prediction accuracy, the STT variant reducing route optimization time by 16%, and the TRL module enhancing recommendation relevance by 12% over time. This study highlights the potential of the proposed framework to enhance personalized tourism experiences while supporting the sustainable management of cultural heritage sites, offering a scalable and adaptable solution for the industry.