Mendeley have also recently open-sourced their recommender framework, which
relies on SGD to train models using scikit-learn, and seems to try to fit
into the sklearn API.
https://github.com/Mendeley/mrec/
Nick
On Mon, Oct 14, 2013 at 1:37 AM, Andreas Mueller
<[email protected]>wrote:
> On 10/09/2013 11:36 AM, Olivier Grisel wrote:
> > Peter implemented "penalized SVD" with SGD for "Netflix
> > challenge"-style matrix factorization problems:
> >
> > http://code.google.com/p/pyrsvd/
> >
> > It should be a pretty good baseline to compare performance against.
> >
> > As for missing data, I would just use scipy.sparse matrices and treat
> > non-materialized zeros as missing data for the sake of memory
> > efficiency and API simplicity.
> >
> I remember there was a thread on how to encode missing values, I think
> for the imputation PR.
> There were three possible scenarios of how to use the sparse matrix
> structure.
> Does anyone have a link?
> I thought the recommendation system API was also discussed there.
>
>
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