Ted, so you do in-memory latent factor computation? I think this is the same technique Koren implied for learning latent factors.
However, i never understood why this factorization must come up with r best factors. I understand incremental SVD approach (essentially the same thing except learning factors iteratively guarantees we capture the best ones) but if we do it all in parallel, does it create any good in your trials? also i thought that cold start problem is helped by the fact that we learn weights first so they always give independent best approximation, and then user-item interactions reveal specific about user and item. However, if we learn them all at the same time, it does not seem obvious to me that we'd be learning best approximation when latent factors are unkown (new users). Also, in that implementation i can't see side info training at all -- is it there? thanks. -D
