Also, once you’ve built up a listening profile, it does automatically generate a listing of other people whose listening habits are similar to yours, as well as a list of artist recommendations for artists you haven’t listened to but might want to check out.

Its ranking strategies aren’t perfect, but they are interesting.

And just because I don’t care if anyone knows what I’m listening to:

http://www.audioscrobbler.com/user/kellyat9/

Maybe we’re talking past each other? I’m not sure.

So what I do instead is make fairly broad smart playlists to use with Party Shuffle and use the ‘play high-rated songs more often’ option combined with manual fiddling with the next-N-songs list every now and then.

It’s still not perfect, but it’s good enough for me.

Isn’t the point of data mining to find interesting data that wasn’t visible at a first glance? Why then produce a list that exactly reflects your likes/dislikes that you know in the first place?

Unless, of course, this is only a first step. Have you tried matching your likes/dislikes against total playtime? Scrutinized your listening habits? Maybe you *do* like Kate Bush after all? That’s what OCD is all about

That said, I *have* to scrobble now. That’s a bit more interesting. What I’d really like to see is something like Scrobbler pointing me to music I’ve never listened to, though. Now *there* is an interesting application…

http://www.audioscrobbler.com/

Er… That aside, one thing that might be useful is to weight ratings by song length–that way, things like the little interstitial pieces that only really make sense when you’re listening to a whole album don’t drag the rating down as much as they might otherwise.

The best I’ve come up with is a smart play list with all my 4s and 5s, + all unranked (which are mostly singles) If the proportion of songs are right it’s OK, but it’s still far from optimal.