Recommender systems are widely used to help users navigate vast content catalogs, but they often limit users to suggestions that closely match their existing preferences, creating "filter bubbles" that discourage exploration. We focus on solving this problem in the context of music recommendations, helping users discover and develop new musical tastes. We embed a knowledge graph containing expert-curated metadata, user interaction data, and audio similarity features, into a representation space where similar songs are mapped closely together. This enables the system to gradually guide users from their current preferences toward a new genre through personalized recommendations. Additionally, we apply a Bayesian active learning approach to iteratively update user preference models based on feedback, balancing exploration and exploitation to ensure user satisfaction while gathering information on the user's new preferences. We conducted a user study to evaluate the approach, demonstrating that a gradual, interactive approach outperforms directly introducing users to a new genre, increasing user engagement and their affinity toward the target genre. This research highlights the value of gradual, user-driven exploration in creating better music discovery experiences. Based on our findings, we provide recommendations for industry stakeholders and discuss opportunities for future research on targeted exploration in music recommendation.