원문정보
초록
영어
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.
목차
1. Introduction
2. Related Work
2.1 Trends in Tourism Recommendation Systems
2.2 Boosting Algorithms
3. Experiments
3.1 Data Preprocessing
3.2 Outlier Handling and Impact Analysis Using Winsorizing
3.3 Theme Classification Model Development
4. Results
4.1 Performance Analysis of the Theme Classification Models
4.2 Performance Analysis of the Satisfaction Prediction Models
4.3 Theme-Specific Satisfaction Prediction Performance
5. System Implementation and Visualization
6. Discussion
7. Conclusion
Acknowledgement
References
키워드
- Theme-Based Tourist Classification
- Char2Vec
- XGBoost
- Gradient Boosting
- Satisfaction-Based Recommendation
- Travel Data Visualization
- Winsorizing