How to normalize results of Singular Value Decomposition (SVD) between 0 and 1?
Singular Value Decomposition is a linear algebraic technique as a result of which the notion of normalization is hard to define. In principle, you can do this normalization by dividing each element A(i,j) of the matrix by the sum (or max) of the elements in that particular (ith) row, i.e. A(i,j) = A(i, j) / \sum_{k=1}^{n} A(i,k)

However, a more elegant way to achieve this would be to apply a probabilistic dimensionality reduction technique such as PLSA or LDA. These dimensionality reduction techniques ensure that you always end up with probability values strictly between 0 and 1.