That's because the recommendation engine that Last.fm used back in the day was made the incredibly expensive way: the entire corpus was hand-tagged and cross-linked by humans atop an enormous CDDB. Last.fm, Audioscrobbler, and MusicBrainz (the association engine) were all linked together.

The recommendations engine used them but it's main strength was it was primarily based on collaborative filtering (https://en.wikipedia.org/wiki/Last.fm).

Essentially if people who listen to many of the same artists/tracks as I do have discovered other things I have not, then those unseen artists/tracks become candidate recommendations.

It worked as well as it did because they had a user base of music fans with a wide variety of tastes. CBS ran them into trouble when they upset those fans by breaking the radio and by being perceived as too close to the RIAA.

The will need to get the numbers up, but I'm hoping them being independent again is a good sign.

But Spotify has that as well. Tons of user curated playlists. And although user playback data is harder to parse through, it's also pretty straightforward to build some clustering algorithm where if you both like X then you might like Y as well.