Is music recommendation difficult?

I think it is a difficult problem. Much of the difficulty is just framing the question in a philosophical sense.

The article talks about music recommendations based on a user's previous music-listening habits but what about recommendations based NOT on previous listening habits? I guess it depends on if the point of music recommendations is to "drill down" on a particular genre or related sub-genre.

In general music recommendations can also be made based on interests in literature or aesthetics or even historical topics. They can also be based on absence of experience-- how do you know if you've optimized globally if you haven't seen the spectrum of possibilities?

Finally, there's the question of what constitutes "success". Is it possible for someone to appreciate a recommendation even if they don't like it (as in "thumbs up" like it)? Aesthetic tastes are far more complex than like/dis-like.

I realize that these considerations aren't necessarily amenable to a pure computational approach. Perhaps the answer lies in using human decisions and computation together, like last.fm scrobbling?