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? -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 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 hessian matrix linearly >> independent... >> >> Thanks. >> Deb >> >> >> >> >> >> On Thu, Mar 6, 2014 at 7:20 PM, Xiangrui Meng <men...@gmail.com> wrote: >> >>> 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 the error still happens when you use a >>> large lambda? >>> >>> Best, >>> Xiangrui >>>
