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Real-Time Context-Aware Recommendation System for Tourism.
1Department of Computer Engineering, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si 13120, Gyeonggi-do, Republic of Korea.
Sensors (Basel, Switzerland)
|April 13, 2023
Summary
This study introduces a real-time tourism recommendation system (R2Tour) that uses machine learning to suggest destinations based on user profiles and changing conditions. R2Tour achieves 77.3% accuracy, enhancing personalized travel experiences.
Area of Science:
- Computer Science
- Artificial Intelligence
- Tourism Informatics
Background:
- The tourism industry is evolving towards 'Tourism 2.0', emphasizing enhanced travel experiences and online information exchange.
- Current tourism recommendation systems struggle with insufficient data and real-time changes, limiting their effectiveness.
- Intelligent tourism service tools are needed for personalized recommendations, time savings, and marketing optimization.
Purpose of the Study:
- To propose a real-time recommendation system for tourism (R2Tour) that provides customized destination suggestions.
- To develop a system capable of responding to dynamic situations, including external factors and geographical information.
- To address the limitations of existing systems in handling insufficient or rapidly changing information.
Main Methods:
- Developed R2Tour, a machine learning-based system utilizing situational data (temperature, precipitation) and tourist profiles (gender, age).
- Trained models to recommend the top five nearby tourist destinations in real-time.
- Evaluated R2Tour's performance using six machine learning models (K-NN, SVM) and data from Jeju Island attractions.
Main Results:
- R2Tour achieved a recommendation accuracy of 77.3%.
- The system demonstrated strong performance with micro-F1 score of 0.773 and macro-F1 score of 0.415.
- The model effectively learned tourism patterns from situational information for real-time recommendations.
Conclusions:
- R2Tour successfully provides real-time, customized tourist destination recommendations by integrating situational awareness and user profiles.
- The system's ability to adapt to changing conditions offers a significant improvement over traditional recommendation engines.
- Future applications include in-vehicle recommendation systems and targeted advertising within the tourism sector.