> presenting it as a viable alternative to LU or QR factorization is pretty... out there. They aren’t presenting it as that. I think you’re just reading it wit

> presenting it as a viable alternative to LU or QR factorization is pretty... out there. They aren’t presenting it as that. I think you’re just reading it with the wrong lens. “Matrix factorization” in machine learning isn’t the same problem as “matrix factorization” in numerical linear algebra. In the linear algebra world, you’re trying to essentially keep the matrix, but get it in a different form. In the ML world, you’re trying to learn to predict something. In general, your loss function isn’t even over all the cells of the matrix (only minimize prediction error over entries where user i actually watched movie j). So the closest numerical linear algebra problem would be factoring a matrix where 99% of the entries are NaN.