There's also a paper from Yahoo! research "Regression-based Latent Factor Models" http://portal.acm.org/citation.cfm?id=1557029
What i like about this is that it doesn't focus on a particular method to combine the models to regress on static profile data + side info. I think it might be combined with methods ALS-WS which unlke SGD are hadoop-parallelizable to do stage computations. It also serves pretty good in situations when there are dyadic interactions but different types interaction context (side info) are available (or sometimes none at all) but static profile information is always available. I think we'll have to get on this problem pretty soon . On Tue, Feb 1, 2011 at 8:24 AM, Ted Dunning <[email protected]> wrote: > And the Mahout-525 github branch of mahout that I started has an apparently > working version for this algorithm. > > I would love to support anyone who wants to do last mile work on that > stuff. > > See https://issues.apache.org/jira/browse/MAHOUT-525 for more info > > On Tue, Feb 1, 2011 at 1:52 AM, Sean Owen <[email protected]> wrote: > > > That Elkan / Menon paper has an elegant theoretical formulation of a > > recommender that uses both ratings and side info at the same time. > > >
