Optimization of Tourism Recommender Systems Through User Behavior Analysis
DOI:
https://doi.org/10.63313/CS.8017Keywords:
Semantic Web, Tourism Recommendation, User Behavior Analysis, Ontology Reasoning, Explainable Recommendation, Intelligent System OptimizationAbstract
With the deep integration of digital tourism and intelligent recommendation, traditional tourism recommender systems face significant limitations in interpreting multi-source user behavior, semantic preference extraction, and explainable recommendation generation. This study presents a com-prehensive review of user behavior modeling approaches, classifying explicit and implicit behavior types and highlighting the semantic Web's advantages in behavior abstraction. A layered recommen-dation architecture is proposed, integrating RDF/OWL ontologies, reasoning engines, and behavioral intent graphs. Context-aware, real-time, and deep learning-based methods are compared in terms of accuracy, latency, and interpretability. Addressing challenges in system scalability and user privacy, the study outlines future directions in GNNs, reinforcement learning, federated profiling, and multi-modal behavior fusion. The findings suggest that semantic-enhanced behavior analysis is pivotal for building intelligent, trustworthy, and sustainable tourism recommender systems.
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