Developing an Adaptive Recommender System Using Reinforcement Learning for Smart Tourism

With the rapid growth of smart tourism, providing personalized travel recommendations has become essential for enhancing tourist experiences. Traditional recommendation systems often rely on static user preferences, failing to adapt dynamically to changing interests and contextual factors. Existing methods, such as collaborative filtering and content-based recommendations, struggle with data sparsity, cold-start problems, and a lack of real-time adaptability to tourists' evolving preferences. These limitations reduce recommendation accuracy and overall user satisfaction. To address these issues, this study proposes a Reinforcement Learning-Based Tourism Recommendation System (RT-RL) that continuously learns from user interactions to generate personalized travel itineraries. The framework integrates contextual bandits and deep Q-learning to optimize recommendations based on user feedback, environmental conditions, and historical preferences. The RT-RL system adapts in real-time, adjusting travel suggestions based on tourists’ changing interests, location, and time constraints. By leveraging reinforcement learning, it improves recommendation accuracy and enhances user engagement. Experimental results demonstrate that RT-RL outperforms traditional methods in providing more relevant, dynamic, and personalized recommendations. The findings suggest that integrating reinforcement learning into smart tourism can significantly enhance travel experiences, making tourism more efficient, enjoyable, and user-centric.