Also, item-item similarity is often (nearly) the result of a matrix product. If yours is, then you can decompose the user x item matrix and the desired eigenvalues are the singular values squared and the eigen vectors are the right singular vectors for the decomposition.
On Sun, Jul 10, 2011 at 2:51 AM, Sean Owen <[email protected]> wrote: > So it sounds like you want the SVD of the item-item similarity matrix? > Sure, > you can use Mahout for that. If you are not in Hadoop land then look at > SVDRecomnender to crib some related code. It is decomposing the user item > matrix though. > > But for this special case of a symmetric matrix your singular vectors are > the eigenvectors which you may find much easier to compute. > > I might restate the interpretation. > The 'size' of these vectors is not what matters to your question. It is > which elements (items) have the smallest vs largest values . > On Jul 10, 2011 3:08 AM, "Lance Norskog" <[email protected]> wrote: >
