I'm building a recommender system and using SVD as one of the preprocessing techniques. However, I want to normalize all my preprocessed data between 0 and 1 because all of my similarity measures (cosine, pearson, euclidean) depend on that assumption.
After I take the SVD (A = USV^T), is there a standard way to normalize the matrix 'A' between 0 and 1? Thanks!
Edit: I want all of my similarity measurements to give results between 0 and 1 and my normalized euclidean distance in particular fails if the input matrix does not have values between 0 and 1.