Tourist Attraction Recommendation System Using a Boosting Algorithm

다국어 초록 (Multilingual Abstract)

We designed a boosting-based tourist attraction recommendation system that integrates theme classification and satisfaction prediction into a single pipeline. Using AI Hub and KMA datasets, we preprocessed tourist destination information and vectorized destination names with Char2Vec. XGBoost was applied for theme classification, achieving high accuracy, while Gradient Boosting regression was used for satisfaction prediction with winsorizing to ensure stability. Experimental results show that the proposed model outperformed other baseline algorithms in both classification and regression tasks. The system visualizes regional theme distributions through Geo and Choropleth Maps, enabling users to explore personalized recommendations intuitively. These results demonstrate that our integrated pipeline can serve as a foundation for future AI-driven personalized tourism recommendation platforms.