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
