Heh? On Tue, May 24, 2011 at 5:12 PM, Dmitriy Lyubimov <[email protected]> wrote:
> so you do in-memory latent factor computation? I think this is the > same technique Koren implied for learning latent factors. > Are you referring to the Log linear latent factor code that I have in my mahout-525 github repo? Or SVD (L-2 latent factor computations)? Or LDA (multinomial latent factor computation)? Or Dirichlet process clustering? Or ...? > 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? > I don't understand the question. Are you asking whether the random projection code finds the best (largest) singular values and corresponding vectors? If so, the answer is yes, it does with high probability of low error. But this doesn't sound like LLL stuff. > 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? > This sounds like LLL again. In LLL, the optimization of side information coefficients and the optimization of the user-item interactions are separately convex, but not jointly convex. This means that you pretty much have to proceed by optimizing one, then the other, then the first and so on. So I don't see how we are learning them all at the same time. You are correct, I think that having lost joint convexity that we don't have strong convergence guarantees, but practically speaking, we get convergence. Regarding the side information, I thought it was there but may have made a mistake. Sorry for being dense about your message.
