wldcordeiro
today at 4:32 PM
I don't think the recommendation engines behind Spotify, Youtube Music, etc compare to the recommendations I got from last.fm over the years. The algorithmic ones seem to have a bunch of issues that bug me as a long time music listener and someone with a large music library.
- their memory is short as hell so you can listen to something for a while, stop and then it'll suggest it to you later as something to "discover"
- they are way too biased towards recently listened music and will replay things over and over if you're not actively managing your queues.
- because they're so based on what you have listened to (recently) they suggest things that are extremely obvious music no one is "discovering"
- they suggest the "top" songs from artists, albums, etc, it's very hard to get it to play a "deep cut"
- if you have a large library you'll inevitably hit playlist song limits and other things silently. Each service handles this differently, Youtube Music seemingly kicks things out of my library or liked playlists each time I add something else.
I've literally just gotten in the habit of never using the autoplay features and just starting whole albums from start to finish again because the algorithms annoy me so much. Youtube Music has been getting worse about it too where now it often ignores the music you chose to start a playlist and starts playing things you've listened to recently regardless of it doesn't match the genre/vibe at all.
That's because the recommendation engine that Last.fm used back in the day was made the incredibly expensive way: the entire corpus was hand-tagged and cross-linked by humans atop an enormous CDDB. Last.fm, Audioscrobbler, and MusicBrainz (the association engine) were all linked together.
But Spotify has that as well. Tons of user curated playlists. And although user playback data is harder to parse through, it's also pretty straightforward to build some clustering algorithm where if you both like X then you might like Y as well.
I switched to Apple Music to save some money and I find the curation and the recommendations to be significantly better than Spotify.
chipotle_coyote
today at 6:11 PM
I've tried to switch to Spotify from Apple Music a few times because the common wisdom seems to be that Spotify has better algorithmic recommendations. But Apple Music "knows" what I like already, and Spotify never grabs me so fast that I'm willing to stick around for weeks training it -- and I suspect part of that is all of Apple Music's human-made playlists. Apple Music has hired a lot of good editors/curators over the years, and I haven't found any service -- including audiophile darlings Qobuz and Tidal -- that beats it in that aspect.
For me curation was better but I was really missing the ability to quickly seed a playlist with a specific vibe and build from there for specific moods.
That, and the desktop app and confusion between library and Apple Music streaming was annoying to manage. They need to unify that experience or split it completely.
zero_bias
today at 5:04 PM
Cannot call lastfm algorithm advanced in any sense. Just opened Amon Tobin page: "similar artists: Kid Koala and DJ Kush", which is an impressively shallow understanding of the last 20 (!!) years of his life, and this happened with almost every artist on the platform, because the average sum of tastes of every listener does not exist in reality. E.g. in the case of Amon Tobin, Kid Koala is the average of similarities between early albums and recent releases, which is just not true, his music cannot be averaged throughout his career. I love my Web 2.0 youth, but the average similarity algorithm doesnt deserve praise. Its not better, its nostalgia and lack of faang-style unlimited greed which confused with better quality
Edit: of course spotify-style recommendations are much much worse, I just mean that lastfm doesnt have good algorithm either because artists are not consistent in releases. What is an average between electronic cult classic "The last resort" and every other Trentemoller album in strict indie rock style? This average does not exist
AlexandrB
today at 4:39 PM
I'm 90% sure that music labels pay to "put their thumbs on the scales" with these recommendation algorithms in order to push their "hot" artists. I wonder how many of these problems are a result of that.
Personally Iām more suspicious of āclassicā artists, where the royalty and songwriting picture might be very skewed behind the scenes. The corporate owners of Spotify favouring one catalog of, say, ā70s musicā versus another could lead to a long-term capture of that category with little reaction or awareness.
Hot artists, in my estimation, are more about bot campaigns to kick off and sweeten āhotnessā as theyāre in an ongoing war against other talent of the moment (with shady labels on all sides).
Every popular spotify playlist has a bunch of good songs and then like one or two "huh?" songs sprinkled in. It's really obvious what's going on.
We can never know for sure if this is or isn't the case, so our only hope for stuff we can be confident isn't this way is with foss / self host able solutions
Using the historical record that they absolutely did this, there is no reason to give them the benefit of the doubt that they are not now doing this.
wldcordeiro
today at 4:58 PM
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The other frustration Iāve noticed is that they key in very heavily on artist and specific āgenreā designation as what feeds the recommendation, which is actually quite bad for anyone who likes experimental work.
I understand that if your recommendations are based on āpeople who like this also tend to like thatā then youāre right in the strike zone. But that approach is basically agnostic to any property of the music itself. Suppose thereās a rock band that released a specific song where theyāre experimenting with a new style that has an atypically (for them) funky/jazzy influence. If I say I want more songs like that I mean songs that fuse rock/jazz/funk, not more songs that fans of [rock band] are into.
I still think for new music discovery Pandoraās approach remains the best if you really curate a station for yourself. Apple Music has been good for creating very listenable playlists though, and their new AI playlist generator has been very fun. Surprisingly, YouTube also seems to have some secret sauce where they recommend a lot of interesting stuff that Iāve genuinely never encountered before. I suspect this is because thereās a lot more amateur and experimental artists on there doing weirder stuff and itās able to find audiences for those in ways that the music-focused services have less visibility into since their catalog is so focused on stuff from the recording industry.