[MivLives]

I think that the issue is that it's always assumed that similar things are a better recommendation. The issue is at least for myself and other people I've talked to about that a lot of times we're looking for something different from the last thing. For example if I spend a week listening to vintage surf rock the recommendations that could be given might be 60s pop, or more surf rock. But what actually I wanted was to listen to experimental jazz with a retro funk twist on it. How could they anticipate that? Talk to anyone who's deep into some art form, movies, tv, music, and you'll see recommendations given that maintain vibes more than just similar things.

[jgwil2]

I think Pandora is one of the better systems out there for this. You prime it with a few examples of stuff you like but then it seems to continually drop slightly different stuff on you to see how you react.

[pilaf]

> Pandora

That's a name I've not heard in a long time. I had no idea they were still in business, I may give it a try.

[selcuka]

> But what actually I wanted was to listen to experimental jazz with a retro funk twist on it. How could they anticipate that?

Supposedly ML should be able to figure that out, by monitoring millions of other people's listening habits. We are not as unique as we think we are. Apparently the models they use are not very good.

[SAI_Peregrinus]

IMO the best recommendation algorithms don't bother recommending things they think one will like; instead they recommend novel things one won't dislike.