On 08/28/2016 01:16 PM, Raphael C wrote:
On Sunday, August 28, 2016, Andy <t3k...@gmail.com
<mailto: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/
<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 \mathbf{P} and \mathbf{Q} such that \mathbf{P} \times
\mathbf{Q} approximates \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 \mathbf{P} and \mathbf{Q} such that
we can reproduce \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.
Depends. Usually you don't want to complete all values, but only compute
a factorization. What do you return? Only the factors?
The PR implements completing everything, and that you can do with the
transformer interface. I'm not sure what the status of the PR is,
but doing that with NMF instead of SVD would certainly also be interesting.
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