On Sunday, August 28, 2016, Andy <t3k...@gmail.com> wrote: > > > On 08/28/2016 12:29 PM, Raphael C wrote: > > To give a little context from the web, see e.g. http://www.quuxlabs.com/ > blog/2010/09/matrix-factorization-a-simple-tutorial-and-implementation- > in-python/ where it explains: > > " > A question might have come to your mind by now: if we find two matrices > [image: > \mathbf{P}] and [image: \mathbf{Q}] such that [image: \mathbf{P} \times > \mathbf{Q}] approximates [image: \mathbf{R}], isn’t that our predictions > of all the unseen ratings will all be zeros? In fact, we are not really > trying to come up with [image: \mathbf{P}] and [image: \mathbf{Q}] such > that we can reproduce [image: \mathbf{R}] exactly. Instead, we will only > try to minimise the errors of the observed user-item pairs. > " > > Yes, the sklearn interface is not meant for matrix completion but > matrix-factorization. > There was a PR for some matrix completion for missing value imputation at > some point. > > In general, scikit-learn doesn't really implement anything for > recommendation algorithms as that requires a different interface. >
Thanks Andy. I just looked up that PR. I was thinking simply producing a different factorisation optimised only over the observed values wouldn't need a new interface. That in itself would be hugely useful. I can see that providing a full drop in recommender system would involve more work. Raphael
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