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From Dmitriy Lyubimov <dlie...@gmail.com>
Subject Re: Recommeding on Dynamic Content
Date Thu, 03 Feb 2011 07:23:10 GMT
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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