Personalized Tourism Recommendation Algorithms Oriented to the Semantic Web
DOI:
https://doi.org/10.63313/CS.8020Keywords:
Semantic Web, Travel Recommendation, Ontology Modeling, Semantic Reasoning, Explainable RecommendationAbstract
With the explosive growth of tourism data and increasingly diverse user demands, traditional recommender systems face limitations in addressing coldstart issues, semantic understanding, and explainability. This study provides a systematic review of personalized travel recommendation approaches oriented to the Semantic Web, with a focus on ontology-based user modeling, semantically enhanced recommendation algorithms, and ruledriven inference mechanisms. The findings highlight the advantages of semantic technologies in knowledge representation and explainable reasoning, particularly in integrating heterogeneous data and deriving complex user interests. The study also outlines future research directions combining large language models and graph neural networks to ad-vance intelligent recommendation systems.
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