The more you curate, the more you define your own taste. It’s then easier to describe what you like in a music and triage.
at the end of the day no matter how many times we beat our heads into the same wall, we’re not even close to an accurate discovery model, music nerds are far better at recommendations than any discovery models. far better.
don’t let their insufferability discourage you. you will be too once you start diving into and going on rants about music which is outside of mainstream fluff. it’s like this with any $subject involving wonks. we’re insufferable to anyone who isn’t into our particular genre of technology. food wonks are insufferable, car geeks are insufferable, gamers are insufferable. that’s ok, if you’re looking for someone who is a geek in a topic, you’re likely to become one too :p just be normal around $subject non-wonks and you’ll be fine.
but yeah, music nerds working in a good record store really do know their stuff.
other places:
- music nerd streams on twitch
- music reviewer youtube channels
- college radio stations (most have an online presence) 770 radiok out of minneapolis is incredible
- kexp out of seattle is absolutely amazing (they’re heavily online as well.)
- just about every mid+ sized city has some amazing radio, usually found in the low FM areas.
at the end of the day though, it’s other people. there are far too many variables for every individual which drives why they may or may not like a song at any given moment. other humans are still absolutely unmatched when it comes to navigating this.
Unless your interests are niche. A fun game I used to play as a teen was going with my parents to the record store and seeing if they had any music I listened to online while my parents shopped. Never found a single CD (but they couldn't be that niche, this story is about bands I found out about from my friends at school!). Employees tried to be helpful, but there's only so much they can do when someone comes in and asks for a list of bands they've never heard of.
If your taste is exactly "maximally dissimilar to anything I have liked before," that's actually pretty easy to calculate from the embeddings as well.
That's gotta be how they do it, right? I'm probably wrong.
Nowadays, they have a quite busy research department so I would imagine that recommendation is quite fancy indeed: https://research.atspotify.com
https://www.canburypress.com/products/you-have-not-yet-heard...
Maybe I'll build that. Sure would be nice to have.
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.
[Disclosure: Work at Google, but not on that. Just thought that course was particularly well-designed.]
No matter what I do in Spotify, under several different rounds of accounts, it always seems to gravitate towards the tastes of the general public, i.e. some form of mass-market pop.
Their recent "ai" assistant was a slight improvement because you can ask it for less popular music which is typically better for music discovery.
Maybe these music services should ask you for music you hate, and start from there instead.
They have a huge sample set of stuff I like just from that (it's still accessibly via YouTube Music, but it sucks). Still, any time I fire up YouTube Music and play a song, the next 2 or 3 can be solidly appropriate and often stuff I wouldn't have thought to play - in a good way.
Then there's a sharp decline where it just starts playing the same thing it played last time or the most common song off the most popular album from the most well known artist adjacent to what played previously.
The whole reason I use the "algorithmic feed" is because it should be optimized for my tastes and what will keep me listening. I still "thumbs-up/down" stuff but it never seems to work. You would think with their insane library and huge dataset of my prefs and listening data they'd be able to generate something great.
Instead I just go back to the human programmed Shoutcast stations I have bookmarked after a few songs.
It's the classic problem of "Ok, but how do I sort these by which ones are actually any good?!"
Are you sure this is an accurate description of your taste? Or do you mean "I'm not interested in things that are similar, but lower quality"?
I'm very much a weird-music enjoyer, and I often have the latter problem where "similar" songs actually just don't capture the same vibe as the truly engaging new song I just heard. But that's not because the algorithm is choosing music that's too similar; it's the opposite. It's trying to choose something similar but can't, so it just picks the next-best thing which I actually don't like.
That annoying dark pattern on a piece of software you use? Because there are people who fall to it, clicking on an add 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 does not need too much mental processing.
I have a peculiar taste in Music. I love many many different types of music, but once I find a really good piece, I'm not interested in things that are very similar to this one. Looks like if we describe musical work with high dimensional vectors, I like to find good vectors that are not too close to each other. But as the author said, Spotify keeps showing me music that's similar to what I've listened. That's exactly what I don't like (with the occasional exception of something being better than the one I've already found and replacing it).
I assume I belong to a peculiar minority. The recommendation algorithms work very well for predictable majorities.
Maybe someday we have an interesting "musical embeddings" model, and then people can implement personalized discovery algorithms using that?