This post is inspired by this tutorial, which talks about interpreting the U and V matrices:
http://www.puffinwarellc.com/index.php/news-and-articles/articles/30-singular-value-decomposition-tutorial.html Given a DataModel that generates preferences between all Users and all Items, lets take two Users and three Items: I I I U 0.5 0.2 0.1 U 0.8 0.3 0.2 What can we learn from an SVD factorization? SVD gives 3 matrices and a scalar: U, a singular value matrix that signifies the actual rank of the matrix, and transpose(V). For simplicity, do the 1-dimensional factorization, which gives left and right vectors instead of scalars. Ignoring the scaling matrix, we get the Left and Right singular vectors. The Left Singular Vector is: (column A x rows U1 and U2) ____A__ U1 U2 The Right Singular Vector is: (row B x columns I1, I2, and I3) ___I1___I2___I3 B Now, the question: what do the Left and Right vectors encode? What do column A and row B mean? -- Lance Norskog [email protected]
