The crux of the argument though, is that if you have a strong CF model with many many ratings, you don't seem to get much benefit with their approach (linear combination of models). That doesn't mean that metadata can't be useful with a different approach. It also doesn't mean that metadata isn't useful for sparse data: in fact, it's incredibly useful, because you don't have much of anything else.
I cannot dispute that metadata can be useful. But it appears, at least for prediction tasks similar to the prize, that an ounce of weak or strong explicit user input is worth a ton of rich implicit data (including item metadata).
