By setting some constraints on which scores were significant, it was possible to build the resulting similarity matrix in less than an hour on a 40-machine cluster
40 machines!?! Wow. We compute our recommendations at Grooveshark in a couple hours on a crappy 2-node Hadoop cluster. hah. We have about as many songs as they have venues, so I have no idea why they need so many nodes. Or maybe we're just extraordinarily harsh in our constraints. I loved this writeup though. Its great to see how other companies tackle these difficult problems.
We also have that problem of a "cold-start" and another one which we call the "coldplay" problem. For them it would be something like McDonalds I guess.