You can tune alpha like any other hyperparam, and measuring whatever
metric makes most sense -- AUC, etc. I don't think there's a general
guidelines that's more specific than that. I also have not applied
this to document retrieval / recommendation before
I don't think you need to modify counts or
In your experience with using implicit factorization for document
clustering, how did you tune alpha ? Using perplexity measures or just
something simple like 1 + rating since the ratings are always positive in
this case
On Sun, Jul 26, 2015 at 1:23 AM, Sean Owen wrote:
> It sounds like you'
We got good clustering results from Implicit factorization using alpha =
1.0 since I thought to have a confidence of 1 + rating to observed entries
and 1 to unobserved entries. I used positivity / sparse coding basically to
force sparsity on document / topic matrix...But then I got confused because
It sounds like you're describing the explicit case, or any matrix
decomposition. Are you sure that's best for count-like data? "It
depends," but my experience is that the implicit formulation is
better. In a way, the difference between 10,000 and 1,000 count is
less significant than the difference
I will think further but in the current implicit formulation with
confidence, looks like I am factorizing a 0/1 matrix with weights 1 +
alpha*rating for observed (1) values and 1 for unobserved (0) values. It's
a bit different from LSA model.
>> On Sun, Jul 26, 2015 at 6:45 AM, Debasish Das
>> w
Yeah, I think the idea of confidence is a bit different than what I am
looking for using implicit factorization to do document clustering.
I basically need (r_ij - w_ih_j)^2 for all observed ratings and (0 -
w_ih_j)^2 for all the unobserved ratings...Think about the document x word
matrix where r_
confidence = 1 + alpha * |rating| here (so, c1 means confidence - 1),
so alpha = 1 doesn't specially mean high confidence. The loss function
is computed over the whole input matrix, including all missing "0"
entries. These have a minimal confidence of 1 according to this
formula. alpha controls how
Hi,
Implicit factorization is important for us since it drives recommendation
when modeling user click/no-click and also topic modeling to handle 0
counts in document x word matrices through NMF and Sparse Coding.
I am a bit confused on this code:
val c1 = alpha * math.abs(rating)
if (rating > 0
