[entropy47]

I wish YouTube Music did this :(

Ideas for other analysis;

Songs that over or under index when sliced by some other dimension. Weather? Morning vs evening? Weekday vs weekend? Summer vs winter?

Songs you never finish vs songs you always finish.

Songs you don't like from albums you do.

One hit wonders (within your personal taste)

Songs you've played 10+ times but haven't heard in over three years

If you had this data at scale: favourite songs from people who enjoy similar niches to you (it's me! Listen to Televators by TMV).

[CrypticShift]

It would be possible to plug an LLM [1] into the stream history and be able to directly type the ideas you cited and get instant results.

> If you had this data at scale

That was the "raison d'etre" of last.fm. Alas, it is not popular anymore (=smaller scale)

> favourite songs from people who enjoy similar niches to you

In last.fm, you can go to an artist's listener page [2], pick a user who "listens to Televators a lot", export their most-listened/loved tracks to Spotify, filter them by genre, and try them out :). You could also go to a track page [3], pick a user who commented on it, and do the same.

[1] https://news.ycombinator.com/item?id=39261486

[2] https://www.last.fm/music/The+Mars+Volta/+listeners

[3] https://www.last.fm/music/The+Mars+Volta/_/Televators

[vintermann]

Google used to be good at data liberation, are you sure you can't get your streaming history somewhere?

If they don't have it, you can do what I did to Spotify many years ago, in annoyance after they closed a request for shared listening history across their apps: I GDPR'd them. See, if you store my listening history at all, sharing it back with me is not optional.

So that's why I know what their Kafka layout looks like. Or what it looked like in 2018 anyway. I like to hope I hastened their implementation of giving users access to their data slightly.