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 > >>> >
