I just moved my scrobbling to a self-hosted instance of Koito after switching from Spotify to Jellyfin. Very happy with the change, as I can still share all my music data with friends
I love last.fm with all my being. I recently created a ListenBrainz (same org as MusicBrainz) account which is an open source alternative that you don’t have to host yourself. I’m scrobbling to both places now just in case.
Check out tapmusic.net too to make cool diagrams out of your scrobbled music.
I'm using selfhosted multi scrobbler [0] to scrobble to lfm, listenbrainz, and selfhosted koito [1].
Maybe not super useful, but fun ;) when at home, I scrobble to MS which distributes the data, when I have no VPN active on the go, I scrobble to last.fm only, which then gets used as source by MS as well, to redistribute it to the others.
If you use Spotify, you can download your full listening history here: https://www.spotify.com/us/account/privacy/. You get it in a pretty convenient JSON format and with a little bit of code it's pretty easy to create some visualizations.
There are also websites for visualizing this data. I'm quite fond of this one: https://explorify.link/. It allows you to do some custom queries.
Last.fm is still used quite a bit, mainly as a listening history tracker rather than a radio or recommendation engine.
Spotify is still the only big streaming service with native platform-level scrobbling. For everything else it's a lot more DIY, usually with third party tools at the device level.
A big reason it’s still relevant is the ecosystem around it. The API hasn't really changed in 15 years, which makes it easy to build tools where a username alone is enough. That kind of lightweight social integration has mostly disappeared elsewhere.
Today, the social / community side is almost entirely just Discord. Nearly every music related server has a bot that displays Last.fm stats. My estimate is that abut 10% of Last.fm their users are also active in Discord music communities.
(Disclaimer: I run .fmbot, a Discord bot that integrates with Last.fm.)
I stopped scrobbling many years ago when they messed together my top artist at the time (the lovely "alan", spelled with all small letters) with other entirely unrelated artists by the same name (but with different letter case, e.g. some "Alan" this, and some "Alan" that.) It didn't represent at all what I was actually listening to, so what was the point?
It has always been like this. It's a super simple system. All artists are only identified by their name. So there are a ton of artist pages out there that actually have to represent multiple artists with the same name. It's kinda silly, but oh well.
https://listenbrainz.org/ is an open source scrobbler, with the advantage that it leverages the musicbrainz database and connects listens to artist and track IDs instead of names, avoiding duplicate confusion. You can keep last.fm and submit to both of you like.
Still scrobbling since 2008. A lot of smaller artists used to upload their music to last.fm, and I found a lot of gems there (specifically in the swedish bitpop scene).
The thing with data is that you have to act on it for it to be useful, and this data is useful only to recommendation engineers. Spotify's end-of-year summary is more than enough to satisfy my curiosity.
Last.fm was probably my first social network, although it probably doesn't make it justice to call it that! I am still scrobbling after so many years! Loved this article. Really good memories... Thx for sharing
The best ”algorithm” for discovering new music was digging through profiles on last.fm back when the social functions of the site were still active. Sure, it was a lot of manual work, but the results were amazing. It wasn't completely blind, I found that people I had high similarity with, it was more likely I'll like what they like, even across different genres. Sometimes people were nice and took the effort to recommend based on my profile. I got introduced to varied music, different genres and even a bit from different countries.
The worst was Pandora, which did recommendations based on breakdown of musical instruments and elements in the song. It did what it aimed to do pretty well, only it was a bad idea. It gave you a lot of uninspiring music that sounded like a bland copy of something you actually liked.
Spotify's recommendations are not super awful, but definitely feel closer to Pandora's style. I wonder why is the result like that even though I'm sure they train their model based on listening history.
In my experience Spotify's song/playlist recommendations are not great, but the album recommendations have a pretty high hit rate. I'm not sure why this would be.
> I found that people I had high similarity with, it was more likely I'll like what they like, even across different genres.
This has been until very recently the modus operandi of most recommendation engine algorithms. If an algorithm is essentially doing what you do, would you not like that?
ooo... i thought Last.fm was a rebranding of audioscrobbler; i didn't know it was a parallel project. and I am an audioscrobbler user since 2006! and I've used it to this day, i mean, last.fm.
When I read the negative take on "it's always somebody else selecting the music for you" I really recoiled. My favorite way to listen to music today is BECAUSE there is someone choosing it for me. I love the human stories behind the music, and it is totally missing with algorithmic stuff. I love Gilles Peterson, and Derek Smith on KMHD, for example, exactly because they are terrific and interesting people and they bring that humanity with their choice of tracks. When they interview people it is so much more interesting as a companion to the music.
My favorite thing about Napster and LimeWire was when you could find a song, and then BROWSE the hard drive of the person hosting that song. It was so interesting to find house music and be digging through the tastes of someone in London. And, then chatting with them, and discovering the live scenes, the people behind the music, etc. I loved that and nothing has ever replaced it.
Having said all this, I am interested in playing with "scrobbling." Anyone have any advice on how to get started? Do you need a music library? Is there a way to import your playlists from YouTube music? I'm not a spotify person.
I miss the old last.fm. I know it's still there, but it's not the same since CBS took over and made everything rely on youtube or whatever it's doing these days.
I worked at Last.fm from 2007 to 2012. The MIR team (think: research) developed a wonderful system called "RadioQL", which allowed you to stitch together custom ratio stations from any of a huge host of factors, joined together by AND, OR, and NOT. You could select artist radios, song radios, tags, and so on, but also combine this with things like the BPM or even some sentiment analysis. It was used a little bit inside some public-facing radio stations, but nobody outside of the staff ever got full access, and that's a tragedy as it was glorious.
Used their API to pull tag data as part of a project not too long ago and had to spend a disappointing amount of time filtering out literal hate tags/slurs and widespread deliberate mistagging.
It caused me to not make the code public until I can ship it with an allowlist. It's almost done but I got distracted
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[ 2.9 ms ] story [ 65.6 ms ] threadhttps://en.wikipedia.org/wiki/Oink%27s_Pink_Palace
Check out tapmusic.net too to make cool diagrams out of your scrobbled music.
Maybe not super useful, but fun ;) when at home, I scrobble to MS which distributes the data, when I have no VPN active on the go, I scrobble to last.fm only, which then gets used as source by MS as well, to redistribute it to the others.
[0] https://github.com/FoxxMD/multi-scrobbler
[1] https://github.com/gabehf/Koito/
If you use Spotify, you can download your full listening history here: https://www.spotify.com/us/account/privacy/. You get it in a pretty convenient JSON format and with a little bit of code it's pretty easy to create some visualizations.
There are also websites for visualizing this data. I'm quite fond of this one: https://explorify.link/. It allows you to do some custom queries.
Spotify is still the only big streaming service with native platform-level scrobbling. For everything else it's a lot more DIY, usually with third party tools at the device level.
A big reason it’s still relevant is the ecosystem around it. The API hasn't really changed in 15 years, which makes it easy to build tools where a username alone is enough. That kind of lightweight social integration has mostly disappeared elsewhere.
Today, the social / community side is almost entirely just Discord. Nearly every music related server has a bot that displays Last.fm stats. My estimate is that abut 10% of Last.fm their users are also active in Discord music communities.
(Disclaimer: I run .fmbot, a Discord bot that integrates with Last.fm.)
Also thanks for your work, while I dislike the spammyness of it, that's on the server owners (main server I'm on limits it to one bot channel)
The thing with data is that you have to act on it for it to be useful, and this data is useful only to recommendation engineers. Spotify's end-of-year summary is more than enough to satisfy my curiosity.
The worst was Pandora, which did recommendations based on breakdown of musical instruments and elements in the song. It did what it aimed to do pretty well, only it was a bad idea. It gave you a lot of uninspiring music that sounded like a bland copy of something you actually liked.
Spotify's recommendations are not super awful, but definitely feel closer to Pandora's style. I wonder why is the result like that even though I'm sure they train their model based on listening history.
This has been until very recently the modus operandi of most recommendation engine algorithms. If an algorithm is essentially doing what you do, would you not like that?
See what your friends are listening to, develop communities around shared musical interests, get better recommendations. Sort of like YouTube now.
very interesting article!
My favorite thing about Napster and LimeWire was when you could find a song, and then BROWSE the hard drive of the person hosting that song. It was so interesting to find house music and be digging through the tastes of someone in London. And, then chatting with them, and discovering the live scenes, the people behind the music, etc. I loved that and nothing has ever replaced it.
Having said all this, I am interested in playing with "scrobbling." Anyone have any advice on how to get started? Do you need a music library? Is there a way to import your playlists from YouTube music? I'm not a spotify person.
It caused me to not make the code public until I can ship it with an allowlist. It's almost done but I got distracted