Live data from Hacker News

The lie of music discovery algorithms

zeynepevecen.dev

31–40 of 198 posts

Re: The lie of music discovery algorithms

#31
post #16

This is great to hear, though I'm curious why photos on your phone / pinterest would be relevant to a recommendation system? Surely the biggest signal would be what Spotify already uses: the features of various relevant factors (your previous listening sessions, your current session, what other similar sessions look like, etc.), that said, their recommendation system is surprisingly terrible given how much easier mus…

YouTube’s algorithm isn’t very good for users because it doesn’t really separate mildly interesting videos that you finish from awesome content you loved. YouTube of course doesn’t care because they don’t make more money when you see something awesome.

It works pretty well for me. Do you use the like and dislike buttons, subscribe to channels you like, etc?

Re: The lie of music discovery algorithms

#32
post #17

In the age of internet, engagement optimization and recommendation algorithms create a new way that we are affected by the behaviour of others. That annoying dark pattern on a piece of software you use? Because there are people who fall to it, clicking on an ad or "engaging" more. That stupid show that keeps being recommended to you? Because a lot of people just sit on the couch, watching something on the list that d…

Yess! Thank you for commenting. I am very interested in this topic. Please do share with me if you find any interesting ways to explore new music for your taste

last.fm & the likes, friends recommendations, asking every guest to put songs in the playlist if I have a party at home

Re: The lie of music discovery algorithms

#33

  "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)

Re: The lie of music discovery algorithms

#34

No music discovery algorithm has satisfied me. All data-driven approaches make predictions based on historical data. Personally I enjoy being exposed to entirely new genres and sounds I've never heard before, instead of variations on genres I've listened to a lot. My solution: listening to NTS, an eclectic online radio station, where diverse artists create playlists.

If entirely new things is what you're looking for, you're not really looking for a recommendation algorithm [1]. What these algorithms try to achieve is finding unknown songs that are in the same genre to what people already like.

[1] Technically "random song not in listen history" would work out, if you'd really like to call that a recommendation algorithm.

Re: The lie of music discovery algorithms

#35

Earlier quoted context omitted.

The setup is pretty basic. I've built a NextJS app, for the LLM model I am using open AI gpt-4-turbo and sending the images there directly without any database for images. I did a little prompting to get the same output everytime and when I get the output I make search on the Spotify API, find the songs and create the playlist with them on your own authenticated spotify account. Likewise I also don't have a database…

To keep your options open, it might be worth switching from GPT to either Gemini or Llama as OpenAI official policies prevent you from training on your logs with the argument this training is “illegal, harmful, or abusive,” so you’d never be able to fine tune or train your own AI in the future on your data or help others do the same. If being permanently locked into a single intelligence service (or risking getting c…

Thanks, that was something I was already considering. Do you know where can I find models specialized in this area? Because it is kinda niche, I couldn't find something that maps images into playlists directly, thats why i went for llms

Re: The lie of music discovery algorithms

#37
post #34

No music discovery algorithm has satisfied me. All data-driven approaches make predictions based on historical data. Personally I enjoy being exposed to entirely new genres and sounds I've never heard before, instead of variations on genres I've listened to a lot. My solution: listening to NTS, an eclectic online radio station, where diverse artists create playlists.

If entirely new things is what you're looking for, you're not really looking for a recommendation algorithm [1]. What these algorithms try to achieve is finding unknown songs that are in the same genre to what people already like. [1] Technically "random song not in listen history" would work out, if you'd really like to call that a recommendation algorithm.

But I also don't want a totally random song eighter. I want something that vibes with me but not directly recommended through my listening history, because then they are extremely similar and feels like they're feeding me the same melodies over and over. Thats why I tried to give the "vibes" in a different format; image, rather than my listening history.

Re: The lie of music discovery algorithms

#38

This is great to hear, though I'm curious why photos on your phone / pinterest would be relevant to a recommendation system? Surely the biggest signal would be what Spotify already uses: the features of various relevant factors (your previous listening sessions, your current session, what other similar sessions look like, etc.), that said, their recommendation system is surprisingly terrible given how much easier mus…

I think spotify's playlists are quite decent. I think they used to be a lot better than they are now, but I suspect that most recommendation systems decay over time. I suspect they don't handle the recommendation feedback very well, so they can start off introducing people to new things, but then become a bit more static and just reinforce the same habits over and over. I think YouTube's recommendations used to be ex…

This is an interesting point and I wonder if part of the issue is that a mature, extremely popular algorithm trends towards the lowest common denominator. I don't mean this as a judgement of taste, it just seems to me that people engage with art in different ways. Maybe the spotify algorithm is perfectly tuned to the majority of people who just want to be able to find more songs that fit the kind of sound they like or find something to throw on in the background. But for a significant minority of others like myself and OP, it's just not tuned to what we actually want from new music.

I also feel the same as you regarding the youtube algorithm. I actually get better recommendations sometimes by just logging out since it will try showing me new stuff.

One thing I'm not sure about is whether it's actually the algorithm's fault or if my expectations have become unrealistic and made me lazy. I used to read magazines and blogs to find new music. There are still tons of people writing about their favorite music, labels that act as curators, etc. I just don't seek them out and instead expect to be spoonfed by the algorithms. Even if this is true though, I suspect many of these algorithms could do a better job.

Also RIP Netflix's old recommendation system. I guess it wouldn't make sense when they can't license every movie like they used to, but I remember it being great. Although maybe it was just pretty good and I was younger and less familiar with the back catalog of good films.

That's the other thing I wonder - am I just getting older and less excited about new things? There used to be a real vitality to finding something new and exciting. Now it kind of feels hard for anything to feel that fresh anymore, it all seems like variations on the same core ideas. I do still find new stuff that I like, but it doesn't have the same thrill. Maybe I'll always be chasing that dragon of youth haha.

Re: The lie of music discovery algorithms

#39

Music discovery has never worked for me, for the simple reason it's the lyrics, not the music. I listen to anyone speaking truth, (the truth I believe, of course), and that gives me a wide disjoint range of music, but they are all singing about political social truths. Marvin Gaye, Public Enemy, Rage Against The Machine, Beyonce, The Stranglers, The Jam, Psychic TV... It's the lyrics, and now today, we finally have t…

You might like folk punk - check out Pat the Bunny.

I feel like there are two kinds of singers - people who are good at singing, and people who have something to sing about. You and I, I think, prefer the latter.

Re: The lie of music discovery algorithms

#40
post #17

In the age of internet, engagement optimization and recommendation algorithms create a new way that we are affected by the behaviour of others. That annoying dark pattern on a piece of software you use? Because there are people who fall to it, clicking on an ad or "engaging" more. That stupid show that keeps being recommended to you? Because a lot of people just sit on the couch, watching something on the list that d…

If you have any taste at all other than "maximally dissimilar to anything I have liked before," there should be a feature that predicts songs you would like.

If your taste is exactly "maximally dissimilar to anything I have liked before," that's actually pretty easy to calculate from the embeddings as well.

Post reply on HN