[Terr_]

It seems some of these services (e.g. Spotify) don't really do musical similarity, but instead emphasize indirect "other fans also like" similarity.

That tends to disregard many reasons you like a particular track, and does especially badly when the liked-track isn't part of a uniform style for an album or artist.

I recognize it's a heck of a lot easier to implement, but it's still a disappointment.

[dropofwill]

They definitely do both, in the public recommendations API you can see vestiges of the old EchoNest acoustic properties along with some new ones they’ve come up with. It’s fun to play around with.

https://developer.spotify.com/documentation/web-api/referenc...

The guy behind Every Noise at Once (engineer at EchoNest/Spotify until the recent layoffs), has some interesting thoughts about this topic:

https://www.furia.com/page.cgi?type=log&id=478

He’s quite biased towards not using ML or acoustic characteristics for recommendations. But even if you disagree it is interesting to hear about how things were working under the curtain (for daylist in this case).