The trouble with "people who listened to X also listened to Y" is it can't ever recommend music that nobody has listened to yet, and is unlikely to recommend anything that doesn't have a reasonable quantity of listeners already, hence likely some level of promotion behind it.

If you select an obscure artist in spotify, the group of people that listen to those might have a few more obscure artists in common. That has worked for me a few times where I go down a rabbit hole of some pretty obscure stuff that is all connected somehow. I have a few things I discovered this way that didn't have more than a few hundred listens.

But you are right that none of this stuff is perfect.

But you have to select an obscure artist first. Hence why the music attention economy is winner-takes-all these days.

At least with spotify I regularly get sent into artists with triple digit numbers of monthly listeners.

Yeah, but I want to hear the long tail of good music with bad promotion and under 10 monthly listeners.

In the old days, hipsters flocked to music with small fanbases of 10,000 or so. Current technology permits us to target down to those in the size of hundreds. And yet, post-hipsters now demand single digit numbers. Scientists hypothesize we may achieve sub-fan levels of popularity at some point, but at what cost?

Don't worry I can be pretentious enough without having to invoke artists you've never heard of; it's not about that!

I have a broader concern for how new artists are supposed to get discovered without a promotion engine behind them. Yes it's always been hard to get started, but the distribution of attention has really become much more top heavy in recent years. I know one guy who played Wembley stadium and still couldn't give up his day job which he was sure he would have been able to do following a gig of that size in the 90s. Yeah so he had a good number of monthly listeners, but it illustrates how the distribution has changed.

Plenty of people on the long tail deserve to be discovered, and use of AI to recommend music - in place of collaborative filtering - really has the potential to fix that.

PS. We were talking monthly listeners weren't we, so you'll be excited to know that fractional fans exist already ;-)

I struggle to imagine what you're saying. In the old days if you had 100 monthly listeners that meant you were likely getting on your local radio station at great effort. You had no shelf space at the record store. You were not searchable on the web. The long tail seems irrefutably better served by modern methods.

Artists struggling to make a living on the back of a single success is, if anything, a product of the longer tails of music being a catered to. The gains are much more spread out now.

It's maybe a niche argument but I'd suggest looking at the one hit wonders of today vs yesterday: https://en.wikipedia.org/wiki/List_of_one-hit_wonders_in_the...

imo the one hit wonders of yesterday were fairly significant hits. The one hit wonders of the 2010s are vastly more ephemeral in my personal opinion. Probably mostly driven by the fact that they used to be conveyed by pop radio and now I don't hear pop radio EVER. But I also have some doubts that most of these 2010s songs will be able to carry a band forward like the one hits of the 90s.

Yes I've been doing the same in bandcamp. If I find something I like, I click on interesting user thumbnails (in the "have it in their collection" section) and listen to some of their collection or wishlist. If this resonates with me I follow them, check out more music and then can jump right to the next user.

While their general recommendations don’t work so well for me, following people I regularly saw writing mini reviews on stuff I bought has worked pretty well to discover older stuff or releases I simply missed (I listen to most new stuff that is up my alley every release Friday anyway). The mini-reviews also help narrow down if it’s even something I want to check out, which works better for me than people who buy without those.

most recs now are based on "users also listened to.." and (very rarely) audio embeddings/features.

however much of my personal discovery is based on trying to understand the history of groups that i piece together from wikipedia and reading about who the artists were.

I want is recommendations based of some sort of in-depth knowledge graph that traces personnel hopping between bands, which other artists worked in the same scene, who they public acknowledge as an influence, etc.

it would be great to uncover things like "hey, did you know that all these songs you like had the same producer? maybe you should dig into other things that this guy produced" or "this artist you loved was really into a performer from a completely different genre -- maybe you should check it to see the influences that they had"

> Vector similarity of artists could be an interesting angle.

Any song has many facets: melody, key, rhythm, dynamicity, voice of the artist, lyrics.

I can see how a music browser of the future (rather than an automatic recommender) would be equipped with many different knobs to turn and tweak each of these dimension's weight (as they are going into a similarity calculation) separately, to give the user control.