Hi Dmitry, I have a feeling the objective may be very close to convex. In that case there are faster approaches than random subsampling.
A common strategy for example is to fit a quadratic onto the previously evaluated lambda values, and then solve it for the minimum. This is an iterative approach, so wouldn't fit well within map reduce, but if you are thinking of doing this as a preprocessing step it would be OK. On Dec 16, 2011, at 10:05 AM, Dmitriy Lyubimov <dlie...@gmail.com> wrote: > Hi, > > I remember vaguely the discussion of finding the optimum for reg rate > in ALS-WR stuff. > > Would it make sense to take a subsample (or, rather, a random > submatrix) of the original input and try to find optimum for it > somehow, similar to total order paritioner's distribution sampling? > > I have put ALS with regularization and ALS-WR (and will put the > implicit feedback paper as well) into R code and i was wondering if it > makes sense to find a better guess for lambda by just doing an R > simulation on a randomly subsampled data before putting it into > pipeline? or there's a fundamental problem with this approach? > > Thanks. > -Dmitriy
