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... > On Mar 11, 2014 7:02 PM, "Xiangrui Meng" <men...@gmail.com> wrote: > >> 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 >> >>> >>
