Warning: this comment has little to do with the article, beyond being a rant on the approach taken by all recommendation engines I've seen.

This an interesting approach, but the objective is similar to most recommendation engines: "Find me something similar to something I like". Sometimes that's a good requirement (e.g. when trying to queue up the next song in a playlist, it's good to have some similarity to the song you're currently listening to). However, when trying to discover new music it's generally a bad approach; since (depending how the requirement is tackled) you'll get recommendations that tend towards some median; i.e.:

  - Other songs by the same artist
  - Songs by artists who have collaborated with the current artist
  - Popular songs (i.e. if almost everyone has a Beetles album in their playlist, getting "people who bought this also bought" recommendations for anything would list Beetles, since technically that's true; it's just uninteresting.
  - Songs in the same genre
  - Songs with a similar sound / structure

i.e. it tends to list things which you're likely to be aware of anyway. Also this means you'll get lots of songs with little variety between them; making your playlists monotonous.

What I'd be really interested in seeing was an engine which finds things on the peripheral; i.e. figures out the things that are likely to appeal to you because of the more unique things you're interested in; or the popular things that you dislike. That way you're likely to get a more eclectic mix of suggestions, and broaden your musical awareness. This would likely produce a lot more false positives initially, as it's expanding your taste range rather than narrowing in on some "ideal" average, so may stray into unknowns; but once you've heard and rated something in this new area, that data can quickly feedback into the algorithm and thus you learn of things you'd previously never have discovered.