My small-sample research into Spotify user satisfaction with new music discovery is that it is a mixed bag. I have not tried it, but the common Spotify dissatisfaction was enough for several of us to investigate novel new music discovery ideas. Nothing forthcoming.
Spotify's algo for discover weekly can be incredible but you have to give it a fuckton of data to work with. My liked songs is >8k I follow probably 1000 artists (the UI doesn't give me a count).
It will surface bands with <20k listeners and songs with <1k plays out from nowhere tailored to exactly what makes the brain go burr.
Except Spotify's discover weekly is stupendously dumb and desperate. I've put 10 years of my music listening into Spotify and it still has no damn clue what I enjoy, and anytime I listen to a song more than a few times, Spotify spends the next 4 weeks trying to cram songs with the same "tags" down my throat.
I had to spend three weeks clicking the "Don't like this" button on EVERY song in my discover weekly before the algorithm figured out "hey, maybe this guy from rural america doesn't actually like songs that are exclusively japanese meme songs"
Like everything in ML, Spotify recommendation works great if you have average or unspecial tastes, because it's just regurgitating the average of your cohort back at you.
If you find any better (particularly opensource) novel music discovery methods can you post about them? I use Spotify solely for its discovery and radio features and find them only ok. Would love to find some other options.
