The overwhelming abundance of tourism information presents a significant challenge for travelers exploring unfamiliar destinations. Visitors often struggle to navigate the vast array of attraction options and face difficulty in integrating scattered attractions into internally coordinated attraction combinations. To address this challenge, we introduce MELT-Rec, a meta-learning-based tourism recommendation system that leverages knowledge from historical user interactions to rapidly adapt to new recommendation tasks, thereby capturing user preferences and providing personalized attraction combinations. This demonstration features a real-time interactive interface that visually illustrates how MELT-Rec rapidly adapts to new user preferences while dynamically generating personalized attraction recommendations.