Graph Neural Network-Based Personalized Tourism Recommendation with Contextual User Preference Modeling
Keywords:
Graph Neural Networks, Personalized Recommendation, Tourism, Context-Awareness, User Modeling, Systems Design, Fairness, PrivacyAbstract
The rapid digitization of the global tourism sector has created an urgent need for intelligent recommendation systems that can guide travelers through overwhelming volumes of heterogeneous options, ranging from accommodations and attractions to multimodal transport services. Traditional collaborative filtering and matrix factorization methods rely on shallow interaction patterns and struggle to capture the intricate, high-order relationships among users, points of interest, contextual signals, and evolving preferences. This paper presents a comprehensive systems-level investigation into the design, deployment, and governance of graph neural network (GNN) architectures for personalized tourism recommendation with a particular emphasis on contextual user preference modeling. We examine how heterogeneous information networks can encode travelers, destinations, temporal dynamics, spatial constraints, social ties, and situational factors into a unified relational structure that is then processed through message-passing GNN encoders. The discussion moves beyond algorithmic novelty to address structural trade-offs including scalability under massive tourist flows, infrastructure requirements for real-time inference, robustness to popularity and exposure biases, fairness across diverse traveler demographics, and privacy compliance under global data protection regulations. Through a synthesis of architectural paradigms, deployment strategies, and policy considerations, the paper develops a holistic framework for building sustainable, trustworthy, and context-sensitive tourism recommender systems that are situated within the broader socio-technical fabric of smart tourism ecosystems.
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