AI Agent-Based Framework for Personalized Music Recommendation

Personalized music recommendation plays a critical role in modern digital media platforms, yet existing systems often lack adaptability, contextual sensitivity, and user transparency. This paper presents an AI agent-based framework designed to deliver real-time, personalized music recommendations by leveraging hybrid modeling and adaptive learning. The proposed architecture integrates collaborative filtering, content-based analysis, and contextual features using a multi-model ensemble comprising XGBoost, neural networks, and audio feature extractors. At its core, an intelligent AI agent employs reinforcement learning to continuously refine recommendations based on user feedback and listening patterns. The system further incorporates FAISS-powered memory for long-term user profiling and SHAP-based explainability to enhance trust and interpretability. Experimental results demonstrate significant improvements in recommendation accuracy, diversity, and user satisfaction over traditional models. This framework offers a scalable, adaptive, and explainable solution for next-generation music recommendation systems.