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Really cool! From my understanding, it looks like this is doing something like word embeddings and searching for nearby points in the embedding space.

Crazy idea: what if you used a dimensionality reduction like t-SNE instead of learning a vector representation? Would you expect similar results?

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Thanks! Yup, this is basically custom music embeddings + nearest-neighbor vector search.

I personally found that vector representations performed significantly better than other approaches.

And the results will actually be a lot better once I ship a better model (the current one can definitely be improved upon).

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