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There might be an interesting open source / self hosting angle to this. Some folks have a large library of music stored locally. Platforms like Roon can give you recommendations on top of this, but are expensive and include a lot of other features.

You could provide discovery services to these users in exchange for model updates and feedback. Couple thoughts on this:

- there are modern techniques to update an ML model at many edge locations, then combine the learnings without violating user privacy. One common application is type-ahead models.

- People who have large local music collections tend to care about music, and would take the time to provide high quality labels for you.

- computers used as media servers often have unused compute cycles because music playback is not that intense and most folks don’t have music on 24/7. You could harness these to reduce training costs for your model

- These libraries would give you access to the long tail of the music catalog, including many things that aren’t on iTunes or other streaming services

- This would also put you in a position to run an open music catalog. Your embedding index would be a key differentiator from existing options.



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