I sometimes wonder if we're looking in the wrong place with music recommendation systems. I've tried both Apple Music and Spotify; it's rare that I hear a song come through on the linear recommendation stream and think to myself "oh my gosh! that's exactly what I wanted!" For me, discovering new music is a branching experience, where I'm constantly listening to little bits of different things, figuring out what I like, and then looking online on forums and blogs to see what's similar to that. It's surprising that the company that owns YouTube, a platform driven by user choice and 'rabbit-hole discovery', would be looking for a new way to feed users linear song recommendations. I would much rather be able to see several 'similar songs' while listening to something, similar to YouTube's recommendation tab. Alas, no streaming service seems to have implemented this (not even YouTube music, afaik).
https://cosine.club/ is the closest I've seen to the ideal branching system. My understanding is that it uses vector embeddings to search for songs that are similar in sound, and it works shockingly well for that purpose. However, it has a limited song database. Also see https://everynoise.com/, which is no longer updated. These use vector embeddings in similar ways, but the exploration experience is controlled by the user, not by a list-generating ranking model. I definitely think that AI-tech is the future of music recommendation, but I would prefer to see more research by large companies in to these user-driven systems, instead of the 'similar autosuggested list', which is, by its very nature, only ever 'good enough.'
I’ve been surprised by how poor Spotify’s recommendations were - they bought the Echo Nest, and seem to have people who are quite smart working on it but when I tried it after Rdio closed no matter what I started with it’d be top 40 after a couple of tracks, enough so that I wondered if there was a background deal with the record labels.
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.
If you’ve ever had the pleasure of using the DJ X feature in Spotify, it does a decent job mixing in some new things I like, but you’re definitely on to something when it comes to popular record labels. I don’t like any of the new pop, but every other “set” that DJ X provides has Chappel Roan or Taylor Swift or Sabrina Carpenter or some other flavor of generic pop I never am interested in. Play counts for some of these popular artists are probably inflated due to that kind of thing.
The best recommendations I get on Spotify are usually via their users that listened to artist X, also listened to artist Y type recommendations. That combined with their list of most popular tracks per artist gives me a rich source of new things to listen to. Their regular recommendations aren't great; it falls into the same "more of the same shit" trap that most other recommendation systems fall into.
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.
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"
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.
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.
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.
> 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.
Same. Back before she blew up in the US I discovered Lorde from a Spotify user generated playlist (i've no idea how i found it, but glad i did) and I played it 100x (it was at the top of the list) and was given the reputation of having good music taste from the person I was dating at the time.
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
Similar, Spotify recommended Alice Merton to me before she went viral not entirely 100% sure but I think it was a user playlist too. There’s been a few other artists I’ve discovered very early in their careers and it’s great seeing them get some fame.
Apples recommendations are so bad. It just goes right back to the same top 100 songs.
I feel like all recommendation systems already do similarity well -- and it's not what I want. True, similarity matters to some extent, but my dream is something that can accurately predict what I'd like. Often I'll only like a song or two from a given artist, so finding artists similar to this artist are often useless.
Related question: I wonder if identical twins are good at recommending each other music
One missing factor here is that the recommendation algo is a prime spot for advertising new music, so all for profit services are very incentivised to introduce tweaks that boost the songs of clients.
YT Music user here. I've found many new artists - across genres - through recommendations. There's also a "related" tab that you can bring up for each song.
I don't know how the youtube recommendation system worked in 2014, but I've definitely had way more interesting and novel things shown then than today, where half the time I'm recommended stuff I've already watched.
I notice that too. youtube recommendation system sometimes recommend already watched videos to me. I haven't tried it yet but I was told that clearing your watch history or using incognito mode can help reset recommendations.
"Understanding the music" alone isn't helpful unless you know what features are relevant for recommendations, and these must be learned from meta- and usage-data.
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.
Apple is rather interesting. On their new music Fridays the playlists alternate predictably between garbage and reasonable stuff. There are perhaps 1-2 reasonable songs on the reasonable stuff list and maybe 1 gets kept.
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.
I don't know what people expect really. Discovering music that resonates with you is not easy. I find spotify gives me about one band I really like every two months and I think that's actually really good. I dislike the vast majority of what it recommends, but I don't think that's a problem.
One resource I use a lot that I didn't mention in my comment is https://rateyourmusic.com/. I find it's most helpful for finding the "canonical" albums & artists in a genre you're unfamiliar with. You can search by genre, influences, and year range, and its listings are generally very accurate. It also just has a culture of having more in depth and well written reviews, so if I'm looking at an album I've never heard of, I often get to read a review by someone who's been listening to it their entire lives. Much more helpful thoughts & opinions than someone whose job it is to review music (though I do enjoy reading some music critics blogs).
I might be a boomer, but I find Youtube Music automatic suggestions superior to Spotify's. It doesn't "branch out" as much as Spotify, but the next song it puts on is always spot-on, "exactly what I wanted"-vibe.
Seconding this, YouTube Music is uncannily good at making radios from songs. It's always what I'm looking for, and when it does branch out, it's usually introducing me to a new jam.
https://cosine.club/ is the closest I've seen to the ideal branching system. My understanding is that it uses vector embeddings to search for songs that are similar in sound, and it works shockingly well for that purpose. However, it has a limited song database. Also see https://everynoise.com/, which is no longer updated. These use vector embeddings in similar ways, but the exploration experience is controlled by the user, not by a list-generating ranking model. I definitely think that AI-tech is the future of music recommendation, but I would prefer to see more research by large companies in to these user-driven systems, instead of the 'similar autosuggested list', which is, by its very nature, only ever 'good enough.'