k)(random.nextGaussian().toFloat)
> val nrm = blas.snrm2(rank, factor, 1)
> blas.sscal(rank, 1.0f / nrm, factor, 1)
> factor
> }
> (srcBlockId, factors)
> }
> }
>
>
> factor is ~ N(0, 1) and then scaled by the L2 norm, but it looks to me the
> abs value is
In the paper “Large-Scale Parallel Collaborative Filtering for the
Netflix Prize”, the following steps are described for ALS:
Step 1 Initialize matrix M by assigning the average rating for that
movie as the first row, and
small random numbers for the remaining entries.
Step 2 Fix M, Solve U by min
Thanks Nick. If this will help other users, I'll create a JIRA and
send a patch.
On 23 March 2017 at 13:49, Nick Pentreath wrote:
> Yup, that is true and a reasonable clarification of the doc.
>
> On Thu, 23 Mar 2017 at 00:03 chris snow wrote:
>>
>> The docum
The documentation for collaborative filtering is as follows:
===
Scaling of the regularization parameter
Since v1.1, we scale the regularization parameter lambda in solving
each least squares problem by the number of ratings the user generated
in updating user factors, or the number of ratings th
