Comment by deklesen
1 day ago
You seem knowledgeable about this.. Care to test my old project for music recommendation? I built it by looking at co-occurrence of artists in Spotify playlists, which gives me word2vec-style vectors, and then its just kNN.
No login needed, just enter some artist names and see what you get:
This is pretty neat, shows good relationships especially on the edgecases where an artist has a very unique sound that other artists dont mimic, but otherwise people who typically like that artist will like others.
Would be very cool if it supported smaller artists than it currently does, because imo thats how you start surfacing emerging talent.
Very interesting, I've been working on a similar project (using word2vec to learn vectors using playlist data), but using songs instead of artists as the 'words'.
The main bottleneck at this point is the volume of data - many songs I'm interested in only are only represented in a handful of playlists, and . Evaluation at any useful scale is also quite difficult. For somewhat obvious reasons, in our AI era Spotify has become quite skittish about letting third parties gain access to their data at scale...