I am basing my knowledge off this paper: http://www.grouplens.org/papers/pdf/webKDD00.pdf
Your book provided algorithms for the user-based, item-based, and slope one recommendation, but none for the SVDRecommender (I'm guessing because it was experimental) Does the SVDRecommender just compute the resultant matrices and follow a formula similar to the one at the top of page 5 in the linked paper? I think I understand the process of SVD but I'm just wondering how it's exactly applied to obtain recommendations in mahout's case. On Apr 18, 2012, at 12:13 PM, Sean Owen wrote: > Yes you could call it a model-based approach. I suppose I was thinking > more of Bayesian implementations when I wrote that sentence. > > SVD is the Singular Value Decomposition -- are you asking what the SVD > is, or what matrix factorization is, or something about specific code > here? You can look up the SVD online. > > On Wed, Apr 18, 2012 at 12:49 PM, Daniel Quach <[email protected]> wrote: >> I had originally thought the experimental SVDrecommender in mahout was a >> model-based collaborative filtering technique. Looking at the book "Mahout >> in Action", it mentions that model-based recommenders are a future goal for >> mahout, which implies to me that the SVDRecommender is not considered >> model-based. >> >> How exactly does the SVDRecommender work in mahout? I can't seem to find any >> description of the algorithm underneath it
