I build OTONAMI , a pitch platform that connects independent artists with music curators — playlist editors, radio DJs, bloggers, label scouts. At its core is a single number: how well does this track fit this curator? The math behind that number is textbook. Cosine similarity, Jaccard, a weighted sum — nothing you can't find in a first-year course. What actually took real, messy music data to get right were three design decisions . Each one came from a concrete failure, and each one is the difference between a matcher that looks fine in a demo and one that ranks sensibly in production. I extracted and generalized the engine into a small, typed, open-source library — music-matching-patterns — so the code below is real and runnable. Here are the three decisions. The shape of the problem A match is scored on three factors and combined with weights: score = genreScore · w_genre + moodScore · w_mood + audioScore · w_audio Enter fullscreen mode Exit fullscreen mode Each sub-score lands in [0, 1] .…