Airbnb's team developed a destination recommendation model to assist users in the early stages of trip planning. This model addresses the challenge of users who haven't yet decided on a destination or travel dates. The model predicts users' destination intent by analyzing their actions on the Airbnb platform, like searches and bookings. A key innovation is integrating diverse signals, balancing active and dormant user behaviors, and incorporating location knowledge. The model uses a transformer architecture, treating user actions as tokens to understand user preferences. Training data is specifically designed to accommodate both active users near booking and dormant users in the initial planning phase. Multi-task learning is used to predict both region-level and city-level destinations, improving location understanding. The model powers autosuggest and abandoned search email notifications, helping users discover potential destinations. In autosuggest, it offers city recommendations, leading to booking gains, particularly in non-English speaking regions. Abandoned search emails feature listings in recommended areas, encouraging booking completion. By focusing on the exploration stage, the model aims to spark inspiration, reduce decision friction, and enhance user engagement. The framework provides a foundation for personalization across the entire trip planning experience, including travel times and price preferences.