Enhancing Serious Game Experience Through In-Game Radio Using Context-Aware Recommender System Based on Player Behavior

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This journal presents a context-aware recommender system (CARS) for in-game radio that adapts music selection based on real-time factors like weather, time of day, player status, and in-game seasons. By leveraging deep learning and hybrid filtering, the system achieved F1 scores up to 0.71 and outperformed traditional recommendation methods. The results highlight how dynamic, context-driven audio can enhance immersion and player engagement through smarter, personalized music experiences.

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