It's very much just a single user system that I haven't generalized because it's a way harder problem.
First, Discogs data quality is actually quite poor once you're trying to build networks and graphs.
Additionally the rules aren't generalizable. Classical music for instance, may have 15 names on the credits, jazz records may have like 4 labels something gets placed on.
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 collection 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... That's a very strong link.
I can do the music I like because I can narrow the ruleset but a general application is basically a winograd schema challenge. This task certainly a neural network problem.
Still working on it. Some day ...
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.
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.
I'm guessing that's because they're both recent breakout bands based on an island . . .
FWIW, the site does not work for my underground bands at all, out of 5 tries 3 didn’t show up at all (Remember Twilight, Der Rest, Jessica's Crime), and 1 (Scythia) has barely any related bands. Only "Die Streuner" got decent results. Some of the bands it’s missing even have wikipedia pages.
Some test cases I used: Logic1000, Tessela, Piezo, TSVI, Two Shell, Caterina Barbieri, Schacke, Amnesia Scanner, Doss, Celyn June [fail], DJ Plead [fail], DVRTN [fail], JakoJako [poor results], LDS [needs disambiguation]
This may already be a better approach, but has anyone built a music recommender that's based on published data (stuff you can find on Discogs) like record labels, aliases, collaborators, remixed-by, etc? As a listener mainly to dance, experimental, and contemporary classical music, I suspect a system based on simple metadata associations would easily beat other approaches.