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From Dmitriy Lyubimov <dlie...@gmail.com>
Subject Re: Recommeding on Dynamic Content
Date Thu, 03 Feb 2011 18:02:34 GMT
Federico, thanks for the reference.

On Thu, Feb 3, 2011 at 1:55 AM, Federico Castanedo <fcastane@inf.uc3m.es>wrote:

> Hi Dimitry,
>
> I'm not sure if this algorithm:
>
> http://www.stanford.edu/~raghuram/optspace/index.html<http://www.stanford.edu/%7Eraghuram/optspace/index.html>
>
> could helps in the case of missing information in SGD, but it seems they
> have a very efficient approach
> in the case of unknown ratings in CF tasks using SVD.
>
> 2011/2/3 Dmitriy Lyubimov <dlieu.7@gmail.com>
>
> > And i was referring to SVD recommender, not SGD here. SGD indeed takes
> > care of that kind of problem since it doesn't examine "empty cells" in
> > case of latent factor computation during solving factorization
> > problems.
> >
> > But I think there's similar problem with missing side information
> > labels in case of SGD: say we have a bunch of probes and we are
> > reading signals off of them at certain intervals. but now and then we
> > fail to read some of them. Actually, we fail pretty often. But regular
> > SGD doesn't 'freeze' learning for inputs we failed to read off. We are
> > forced to put some values there; and least harmless, it seems, is the
> > average, since it doesn't cause any learning to happen on that
> > particular input. But I think it does cause regularization to count a
> > generation thus cancelling some of the learning. Whereas if we grouped
> > missing inputs into separate learners and did hierarchical learning,
> > that would not be happening. That's what i meant by SGD producing
> > slightly more erorrs in this case compared to what  it seems to be
> > possible to do with hierarchies.
> >
> > similarity between those cases (sparse SVD and SGD inputs) is that in
> > every case we are forced to feed a 'made-up' data to learners, because
> > we failed to observe it in a sample.
> >
> > On Wed, Feb 2, 2011 at 11:05 PM, Ted Dunning <ted.dunning@gmail.com>
> > wrote:
> > > That is a property of sparsity and connectedness, not SGD.
> > >
> > > On Wed, Feb 2, 2011 at 8:54 PM, Dmitriy Lyubimov <dlieu.7@gmail.com>
> > wrote:
> > >>
> > >> As one guy from Stanford demonstrated on
> > >> Netflix data, the whole system collapses very quickly after certain
> > >> threshold of sample sparsity is reached.
> > >
> > >
> >
>

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