For alm I have started experimenting with the following:
1. rmse and map improvement from loglikelihood loss over least square loss.
2. Factorization for datasets that are not ratings (basically improvement
over implicit ratings)
3. Sparse topic generation using plsa. We are directly optimizing
On Wed, Mar 25, 2015 at 7:59 AM, Debasish Das wrote:
> Hi Xiangrui,
>
> I am facing some minor issues in implementing Alternating Nonlinear
> Minimization as documented in this JIRA due to the ALS code being in ml
> package: https://issues.apache.org/jira/browse/SPARK-6323
>
> I need to use Vector
Hi Xiangrui,
I am facing some minor issues in implementing Alternating Nonlinear
Minimization as documented in this JIRA due to the ALS code being in ml
package: https://issues.apache.org/jira/browse/SPARK-6323
I need to use Vectors.fromBreeze / Vectors.toBreeze but they are package
private on ml
There is a usability difference...I am not sure if recommendation.ALS would
like to add both userConstraint and productConstraint ? GraphLab CF for
example has it and we are ready to support all the features for modest
ranks where gram matrices can be made...
For large ranks I am still working on
The current ALS implementation allow pluggable solvers for
NormalEquation, where we put CholeskeySolver and NNLS solver. Please
check the current implementation and let us know how your constraint
solver would fit. For a general matrix factorization package, let's
make a JIRA and move our discussio
Hi,
I am bit confused on the mllib design in the master. I thought that core
algorithms will stay in mllib and ml will define the pipelines over the
core algorithm but looks like in master ALS is moved from mllib to ml...
I am refactoring my PR to a factorization package and I want to build it on
