> For me at least, if I am listening to, say, Kraftwerk, a good suggestion for next artist to listen to is not necessarily the artists most people who like Kraftwerk like.
There are several separate things here:
The article, as far as I can tell looks at "if you like X, you're more likely to like Y".
You're describing "if you enjoyed listening to X right now, you're likely to enjoy listening to Y right now" and "if you like X and Y, you're like user B, who also likes Z".
These are all wildly different, and are useful for different purposes. The first is great if you don't have more data on the user than what they picked right now. The other two are better for different applications if you have the data.
One pet peeve of mine is that most music players seems to only take into account user similarity, rather than what you're likely to like right now. E.g. I rarely want to suddenly transition e.g. from a slow classical track to a super-noisy 8-bit war game chip tune even if I like both. And some transitions are great at some times of day, but not so great e.g. when I want a specific type of music because it works best for me when I'm working.
It greatly annoys me when I use of these services and they make transitions that are "obviously" wrong. If I've skipped the last 5 noisy tracks and listened to every slow track, clearly I want slow music right now even though I've previously loved all those noisy tracks...
It must be possible to do so much better... Even just mixing in the other recommendation data with some simple markov chains you'd think would help.
There are several separate things here:
The article, as far as I can tell looks at "if you like X, you're more likely to like Y".
You're describing "if you enjoyed listening to X right now, you're likely to enjoy listening to Y right now" and "if you like X and Y, you're like user B, who also likes Z".
These are all wildly different, and are useful for different purposes. The first is great if you don't have more data on the user than what they picked right now. The other two are better for different applications if you have the data.
One pet peeve of mine is that most music players seems to only take into account user similarity, rather than what you're likely to like right now. E.g. I rarely want to suddenly transition e.g. from a slow classical track to a super-noisy 8-bit war game chip tune even if I like both. And some transitions are great at some times of day, but not so great e.g. when I want a specific type of music because it works best for me when I'm working.
It greatly annoys me when I use of these services and they make transitions that are "obviously" wrong. If I've skipped the last 5 noisy tracks and listened to every slow track, clearly I want slow music right now even though I've previously loved all those noisy tracks...
It must be possible to do so much better... Even just mixing in the other recommendation data with some simple markov chains you'd think would help.