The reason this simple mechanism works so well is that it gets rid of personal biases and instead taps into a community of listeners listening to the same stuff. Confirmation bias is the core issue here. I don't want confirmation bias. I want my biases challenged with new things. Not randomly new but based on what others are listening to that listen to similar things. And not just randomly based on everything I listen to but on specific things that I'm playing.

Vector similarity of artists could be an interesting angle. But it would probably risk pulling out a lot of cover bands and imitators. You want stuff that is close but not too close.

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

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 ;-)

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.

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"

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.

Considering the pool of music on the radio when I was a kid, that's a reasonable hit rate in my mind. I'm not sure I would cope with an influx of music larger than that.

It requires the music to be already present, however, so not ideal for finding new music.

Genre, tempo, key, vocalist sound, instruments, and so on. These might all be relevant in different recommendations, at different times, in some particular order depending on the user. The music-content in effect only serves to align tracks along lines in the embedding space.

Algorithmic playlists I've not found useful. The Apple Music "create a station from this song" feature is more or less broken imo -- i get so much of the same same same stuff

Apples recommendations are so bad. It just goes right back to the same top 100 songs.

Apple Music is notably better – and has the benefit of not funneling your money to the likes of Rogan – and the recommendations will be fairly good within a genre but it does overweight your library a bit (I wish it had a “I’m looking for something new” / “familiar” toggle).

I am curious what Rdio did differently as I had a very good success rate with their suggestions and it seems unlikely that there was some secret sauce nobody else has been able to figure out.

Have you tried the “Discovery Station” on Apple Music? It’s supposed to play only music new to you. It’s fairly new and was introduced in summer 2023.

Related question: I wonder if identical twins are good at recommending each other music