The rapid expansion of digital tourism and traveler data presents a significant opportunity for personalization, yet existing systems struggle to adapt to ever-changing tourist preferences within context. This study proposes a novel multi-dimensional artificial intelligence (AI) framework leveraging advanced machine learning for hyper-personalized, real-time tourism experiences. The architecture integrates transformer-based neural networks, reinforcement learning agents, and ensemble methods to process heterogeneous data streams, including user behavior, social media, environment, time, and culture. The method used was a hybrid method combining the collaborative filtering recommendation system along with the content-based recommendation system along with improvement using real-time adaptation and optimization algorithm. As more and more tourists started utilising the tech support service, the framework was validated through a large deployment at various tourism ecosystems. Also, the analysis included the recognition of 2.3 million users interactions and 450000 during service transactions in 15 nations. Results show a significant increase in recommendation accuracy for single-item recommendations (78% improvement), personalization accuracy (91.2%), and satisfaction score (65% increase) compared to conventional systems. The system based on AI take less than 0.4 s for a response while reducing computing overhead by 42% through algorithm. This study introduces an innovative approach to intelligent tourism systems that utilizes real-time contextualized information and predictive behavior models to improve tourist satisfaction. Innovative mathematical frameworks for modelling tourism preferences and new cross-cultural adaptation techniques. The practical implications are better experience for customers, revenue maximization for tourism operators and sustainable tourism development through optimal allocation of resource.
Yingjie Wang (Wed,) studied this question.