-, Shalini Priya (2024) Intelligent Travel Solutions: Merging User Preferences with Real-Time Contextual Awareness. Masters thesis, Dublin, National College of Ireland.
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Abstract
The travel industry increasingly leverages technology for personalized user experiences. This study develops a dynamic recommendation system to address limitations in static models that fail to meet diverse traveler needs. Using geotagged social media data, behavioral patterns, and situational factors, the system significantly enhances recommendation accuracy and relevance. The methodology integrates advanced techniques: LightGCN for collaborative filtering, DBSCAN for clustering, and matrix factorization for filtering user-item interactions. Performance validation is conducted using robust metrics, including Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE),and Root Mean Square Error (RMSE), ensuring reliability. Findings highlight the system’s ability to deliver precise, adaptive travel suggestions, improving decision-making and user satisfaction. The hybrid approach transforms traditional travel planning, making it more engaging and responsive to user preferences. By addressing dynamic traveler needs, the model strengthens customer loyalty and provides a competitive edge in the evolving travel market.
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