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The lie of music discovery algorithms
81–90 of 198 posts
Re: The lie of music discovery algorithms
#82So I've been using Tidal for 5 years now, and feel they run circles around Spotify in terms of curation. Their algorithms and curated tracks are better, and they steer away from the social/gamification features and lean in to artist-centric features. For example, they've had a "credits" feature since day one - you can look up the producer, guitarist, oboeist, etc of any song, and see what other work they've done. In terms of discovering new music, there is absolutely no comparison to being able to look up the actual human behind the track. I've discovered hundreds of bands this way.
Re: The lie of music discovery algorithms
#83Earlier quoted context omitted.
I'm not sure how the Spotify recommendation algorithm works at all, but for some reason I imagined them doing fancier things than looking at my liked songs and finding similar ones. I would've thought they'd build a profile of you, and then find similar user profiles and show you songs those folks liked that you hadn't found yet. That's gotta be how they do it, right? I'm probably wrong.
I feel those are how Pandora and Last.fm (used to?) work respectively. Nowadays everything seems to just put a bunch of tags on a track and suggest you things with the same tags to the tracks you liked. Doesn't even need to match the same combination of tags, just some number of them. The problem is, you probably care about the small, specific tags, and the system cares about wide "popular" tags. If you like a couple…
I wish I had a music recommendation service built on Pandora's immense dataset of music tags that could build me a playlist that I could link back to whichever music service I happen to be using at the time. I could have it do things like require at least 3 tags in common between adjacent tracks such that it could jump around between 2 dozen genres but the transaction between any 2 given tracks isn't too jarring. It'd also be nice if I could tell it to make a playlist where every song shares one particular tag in common.
Maybe I'll build that. Sure would be nice to have.
Re: The lie of music discovery algorithms
#84"They are not suggesting new, very interesting melodies. They are finding you the tweaked versions of the songs you already like and, even on your first listen you can predict the melody that’s to come." I really don't think that's the main method of Apple Music or Spotify to create a list of suggestions. From what I know, (beside of dark marketing-patterns) the suggestions are created by checking what other songs pe…
I actually do find this observation to be quite accurate for many of my own 'suggestions.' I'm regularly recommended 'new' and 'old' music that was clearly matched to my 'tastes' only by melody or, more noticeably, sample. It very much seems like if a song fits into a genre I listen to frequently or have been listening to lately, and it samples another song I've listened to before—cheap recommendation. And the greater the frequency of individual plays (i.e. the more times I've replayed any one song), the more likely that derivatives will be recommended to me.
It's easy to see how this would've been baked into a human-made algorithm when you consider waveforms. Speaking only to Spotify's algorithm here. And it doesn't really bother me for obvious reasons. But it is creating something of a musical echo chamber for me.
Re: The lie of music discovery algorithms
#85The two issues I've had with every discovery algorithm:
"We have [favorite band] at home" - it picks things you like from your favorite band - instruments, tempo, etc then finds bad knockoffs that are superficially similar but painful to listen to.
The "Iron and Wine" problem - some bands are so generic that they tick every single similar box and flood your recommendations. For years, it didn't matter what band/genre I tried to find recommendations from, I got Iron and Wine.
Re: The lie of music discovery algorithms
#86> They are not suggesting new, very interesting melodies. They are finding you the tweaked versions of the songs you already like and, even on your first listen you can predict the melody that’s to come This seems like the complaint of somebody who hasn't been using spotify very long. After a decade plus, I feel like my algorithm is a rich compost pile of all of my previous phases of music. Spotify is excellent at le…
This is my experience as well... I have a very broad music taste but with some main themes. I find Spotify's algorithm (11 years of Premium) to regularly surface things I'll like, whether new music from artists I already know, music correlating strongly with known tastes, or every once in a while something that seems out of distribution but I like it anyway!
It probably helps that the strongest areas of my taste are relatively small or niche genres, like Scottish trad and Celtic (folk) rock. In those niches, similar-but-different is often distinctively different in actual experience. Sure, there's covers of the same song from time to time, but I actually do like enough of those not to be bothered, if they bring something new.
Re: The lie of music discovery algorithms
#87Music has so much power over people!
Unfortunately it's an extremely unprofitable industry, with very little revenue in general, and that little revenue shared by a few duopolists. Although it's an art, it's capitalistically treated like a craft, successful practitioners serving the same, safe, "good-enough", risk-free, pleasant sound again and again. I like offensive, avant-garde, creative, novel, strange music and (1) artists I love live in immense poverty (i.e. artist life) (2) even though this is the biggest passion of my in life and I think I do have some skills, working on music is 99.999...% surefire way to have financial hardship.
Re: The lie of music discovery algorithms
#88In the age of internet, engagement optimization and recommendation algorithms create a new way that we are affected by the behaviour of others. That annoying dark pattern on a piece of software you use? Because there are people who fall to it, clicking on an ad or "engaging" more. That stupid show that keeps being recommended to you? Because a lot of people just sit on the couch, watching something on the list that d…
I'm not sure how the Spotify recommendation algorithm works at all, but for some reason I imagined them doing fancier things than looking at my liked songs and finding similar ones. I would've thought they'd build a profile of you, and then find similar user profiles and show you songs those folks liked that you hadn't found yet. That's gotta be how they do it, right? I'm probably wrong.
I don't work at Spotify anymore and I didn't work on the tech I'm describing, but I picked up a bit about what was going on while there.
First, there is/was no single algorithm, but the core ideas driving a lot of recommendations is:
1. Create user taste vectors
2. Match those vectors to other users or collections of tracks
3. Use that information and combinations of other things to find recommendations.
Each step of the process is constantly being experimented with. Different custom playlists might be using a different combination of tech doing those basic steps.
Re: The lie of music discovery algorithms
#89"You need to enable JavaScript to run this app."
What do you mean, "app"? There's no "app" there.
If anyone constructed a PDF, which was itself blank but, via embedded JavaScript, loaded parts of itself from a remote server, people would rightly balk and wonder what on earth the creator of this PDF was thinking — yet this is precisely the design of many “websites” [1]
Re: The lie of music discovery algorithms
#90I've found YouTube Music's recommendations very good. I somewhat routinely do 14 hour drives and I always end up hearing three or four new songs I love. As I've listened to various songs it's done a great job of figuring out what within the genre I'm listening to I enjoy and don't enjoy. The idea of knowing the next melody, as the author says, doesn't really bother me if I like the track, though my taste isn't very diverse.