How to Build a Personalized AI Music System From Your YouTube Watch History

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A developer has outlined a multi-month project to create a self-learning music recommendation system powered by a user's own YouTube viewing behavior. The system involves a browser extension that silently tracks watch time, skips, and replays on YouTube videos to capture implicit listening preferences. This data is sent to a FastAPI backend, stored in PostgreSQL, and processed nightly by machine learning pipelines that classify content, resolve song metadata, and generate audio embeddings. The goal is to produce personalized playlists that reflect actual listening habits rather than platform-driven recommendations. The project is designed as a full-stack learning exercise spanning browser extensions, backend APIs, databases, and ML models.

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