this post was submitted on 08 Dec 2024
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Interesting - I don't use Spotify anymore, but I overheard a conversation on the train yesterday where some teens were complaining about the results being super weird, and they couldn't recognize themselves in it at all. It seems really strange to me to use LLMs for this purpose, perhaps with the exception of coming up with different ways of formulating the summary sentences so that it feels more unique. Showing the most played songs and artists is not really a difficult analysis task that does not require any machine learning. Unless it does something completely different over the past two years since I got my last one...
You want to dimension reduce to get that "people who listen to stuff like you also like to listen to" recommendation. To have an idea whom to play a new song to, you ideally want to analyse the song itself and not just people's reaction to it and there we're deep in the weeds of classifiers.
Using LLMs in particular though is probably suit-driven development because when you're trying to figure out whether a song sounds like pop or rock or classical then LLMs are, at best, overkill. Analysing song texts might warrant LLMs but I don't think it'd gain you much. If you re-train them on music instead of language you might also get something interesting, classifying music by phrasal structure and whatnot don't look at me I may own a guitar but am no musician. And, of course, "interesting" doesn't necessarily mean "business case" unless you're in the business of giving Adam Neely video ideas. "Spotify, play me all pop songs that sing 'caught in the middle' in the same way"... not a search that's going to make spotify money, or anyone asked for.
They are using LLM's because the companies are run by tech bros who bet big on "AI" and now have to justify that.