Another question: do you have negative or out-of-range user or product
ids or? -Xiangrui
On Tue, Mar 11, 2014 at 8:00 PM, Debasish Das debasish.da...@gmail.com wrote:
Nope..I did not test implicit feedback yet...will get into more detailed
debug and generate the testcase hopefully next week...
Nope...with the cleaner dataset I am not noticing issues with the dposv and
this dataset is even bigger...20 M users and 1 M products...I don't think
other than cholesky anything else will get us the efficiency we need...
For my usecase we also need to see the effectiveness of positive factors
They have been merged into the master branch. However, the
improvements are for implicit ALS computation. I don't think they can
speed up normal ALS computation. Could you share more details about
the variable projection?
JIRAs:
https://spark-project.atlassian.net/browse/SPARK-1266
Hi Deb, did you use ALS with implicit feedback? -Xiangrui
On Mon, Mar 10, 2014 at 1:17 PM, Xiangrui Meng men...@gmail.com wrote:
Choosing lambda = 0.1 shouldn't lead to the error you got. This is
probably a bug. Do you mind sharing a small amount of data that can
re-produce the error?
Choosing lambda = 0.1 shouldn't lead to the error you got. This is
probably a bug. Do you mind sharing a small amount of data that can
re-produce the error? -Xiangrui
On Fri, Mar 7, 2014 at 8:24 AM, Debasish Das debasish.da...@gmail.com wrote:
Hi Xiangrui,
I used lambda = 0.1...It is possible
Hi Xiangrui,
I used lambda = 0.1...It is possible that 2 users ranked in movies in a
very similar way...
I agree that increasing lambda will solve the problem but you agree this is
not a solution...lambda should be tuned based on sparsity / other criteria
and not to make a linearly dependent
Hi,
I am running ALS on a sparse problem (10M x 1M) and I am getting the
following error:
org.jblas.exceptions.LapackArgumentException: LAPACK DPOSV: Leading minor
of order i of A is not positive definite.
at org.jblas.SimpleBlas.posv(SimpleBlas.java:373)
at
I'm not sure about the mathematical details, but I found in some
experiments with Mahout that the matrix there was also not positive
definite. Therefore, we chose QR decomposition to solve the linear system.
--sebastian
On 03/06/2014 03:44 PM, Debasish Das wrote:
Hi,
I am running ALS on a
If the matrix is very ill-conditioned, then A^T A becomes numerically
rank deficient. However, if you use a reasonably large positive
regularization constant (lambda), A^T A + lambda I should be still
positive definite. What was the regularization constant (lambda) you
set? Could you test whether
