Comment by rickdeckard | Hacker News Reader

Comment by rickdeckard | Hacker News Reader

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  "They are not suggesting new, very interesting melodies. They are finding you the tweaked versions of the songs you already like and, even on your first listen you can predict the melody that’s to come."

I really don't think that's the main method of Apple Music or Spotify to create a list of suggestions. From what I know, (beside of dark marketing-patterns) the suggestions are created by checking what other songs people like/listen to who ALSO like/listen to this current song (or other songs you played), and the common neighbors of those songs in other playlists.

(If you play music for your toddler, your future suggestions will include children's music not because it sounds similar but because "a critical mass of other people who listened to Baby Shark on repeat also listened to: Old Town Road")

  It is weird and it’s ironic that they call that “discovery”, as it feels more like variations of what I'm already listening to.

This indicates that the persona that this platform created for you is quite homogenous and probably matches closely with many other personas on the platform, so many people who listen to the same music as you do apparently listen to _nothing else_ than this kind of music...

(not trying to defend those suggestion algorithms, just analyzing the comment)

> This indicates that the persona that this platform created for you is quite homogenous and probably matches closely with many other personas on the platform

To add to this analysis, I think there may also be a feedback component to this problem that exacerbates the issue, since most users are passively using the suggestion algorithm.

In other words, if the suggestion algorithm tends to create a homogenized persona of the user's taste, say, because they don't bother to actively correct it, then this persona is embedded into a cluster of people with similar personas. And because the persona is now closer to said cluster, the suggestions will become even more homogenized. Moreover, since the cluster is mostly composed of passive users, the cluster itself will tend shrink (eg in variance) and to get more homogeneous.

I suspect that most algorithms do not do enough to prevent this global trapping effect, and so even if they have some method to sample "something new" for the user this becomes less and less efficient as more users rely on the algorithm for their suggestions.

I actually do find this observation to be quite accurate for many of my own 'suggestions.' I'm regularly recommended 'new' and 'old' music that was clearly matched to my 'tastes' only by melody or, more noticeably, sample. It very much seems like if a song fits into a genre I listen to frequently or have been listening to lately, and it samples another song I've listened to before—cheap recommendation. And the greater the frequency of individual plays (i.e. the more times I've replayed any one song), the more likely that derivatives will be recommended to me.

It's easy to see how this would've been baked into a human-made algorithm when you consider waveforms. Speaking only to Spotify's algorithm here. And it doesn't really bother me for obvious reasons. But it is creating something of a musical echo chamber for me.

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