That's exactly what I've been working on. Choose it based on the people someone chose to work with and those networks.
It's very much just a single user system that I haven't generalized because that's a way harder problem, the rules aren't generalizable. Classical music for instance, may have 15 names on the credits, a jazz record may have like 4 labels it gets placed on.
Let's take an EP by a popular artist. It may have remixes by popular DJs. Those links are poor quality. Now if it's by an unknown artist, those links usually become high quality.
So do you follow the network of the guy playing the oboe? Maybe? Sometimes weird connections like the album artist is the strong link, sometimes it's compilation albums that one of the songs is placed on. Take say the 1992 release Trancemaster 1: https://www.discogs.com/release/54719-Various-Trancemaster-V... the clustering of those artists is a very strong high quality link. And then there's the "that's what I call music" type compilations where they're worthless.
I can do the music I like because I can narrow the ruleset but a general application is basically a winograd schema challenge because there's a large body of intuition required to weight the network. This task is certainly a nontrivial neural network problem.
Doing it manually with human discretion works. I've got tools for doing that and large labeled data sets I've been working on for 4 years. It just doesn't generalize.
That makes sense. I threw in my favorite (used to be underground) artist and saw every side-project they had pop up (and they have around a dozen). OTOH, I just threw in Nujabes and saw a number of people I do not recognize, which I find interesting.
From my tests, the suggestions make sense. It seems to be better than the spotify "I will play things that send you into an echo chamber" algorithm.
There's two approaches: musicologist and popularity.
The musicologists do a serious study and have sophisticated tools I can't pretend to understand but their results sound similar: so similar that the serendipity and adventure is sucked out of it.
Also they suffer from the generality problem as well. Qualities that matter in one genre, such as airy female vocals in a minor key, or whatever, are absolutely irrelevant for another genre. Take for instance, Irish folk music where it may be signal and say, Acapella, where it may be noise. Thus considering them is both right and wrong and we're back to our problem.
If you want to categorize the music to "fix" the problem, you run into binning issues. Let's say early 90s hardcore; they can have trance, breakbeat, dnb, house and jazz sections in a single song. Good luck trying to use your genre based contextual mapping on an unsupervised model.
The popularity approach, which ignores the content, is deceptive because it appears in many forms: people who listened to X also listened to Y or Y is trending or any of a number of variations where some magnitude of humans or temporal delta is used as signal.
These all tend towards the not long end of the long tail and so you eventually get the same mediocre experience - you start with your obscure prog rock group from the 1960s and 20 songs later you're at Cream or Hendrix. The "solution" is to tamper the drift via clustering but it will tend the same directions.
In practice though, these approaches service the majority of tastes, that is familiarity, so they're fit for purpose.
Nice! Glad you're doing this work. Sounds like something that would be best integrated into a community with strong contributor culture that could do the tagging, but that's another task entirely.
It's very much just a single user system that I haven't generalized because that's a way harder problem, the rules aren't generalizable. Classical music for instance, may have 15 names on the credits, a jazz record may have like 4 labels it gets placed on.
Let's take an EP by a popular artist. It may have remixes by popular DJs. Those links are poor quality. Now if it's by an unknown artist, those links usually become high quality.
So do you follow the network of the guy playing the oboe? Maybe? Sometimes weird connections like the album artist is the strong link, sometimes it's compilation albums that one of the songs is placed on. Take say the 1992 release Trancemaster 1: https://www.discogs.com/release/54719-Various-Trancemaster-V... the clustering of those artists is a very strong high quality link. And then there's the "that's what I call music" type compilations where they're worthless.
I can do the music I like because I can narrow the ruleset but a general application is basically a winograd schema challenge because there's a large body of intuition required to weight the network. This task is certainly a nontrivial neural network problem.
Doing it manually with human discretion works. I've got tools for doing that and large labeled data sets I've been working on for 4 years. It just doesn't generalize.
Some day ...