In the so-called incremental SVD used for collaborative filtering:
The user x item matrix R is factored as QP using gradient descent. In the classical SVD there is the diagonal matrix S which holds the singular values. What happens(ed) to that matrix in this formulation? Is it just omitted and they still call it SVD or is it implicitly part of Q and/or P?