SVDRecommender is intriguing, thanks for the pointer. On Sun, Jul 10, 2011 at 12:15 PM, Ted Dunning <[email protected]> wrote: > 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: >> >
-- Lance Norskog [email protected]
