I was reading this paper: "Combining Predictions for Accurate Recommender Systems" http://www.commendo.at/UserFiles/commendo/File/kdd2010-paper.pdf
and one particular method used to blend different recommenders is KRR (Kernel Ridge Regression). The authors had the followings conclusion about it: "KRR is worse than neural networks, but the results are promising. An increase of the training set size would lead to a more accurate model. But the huge computational re- quirements of KRR limits us to about 6% data. The train time for one KRR model on 6% subset (about 42000 samples) is 4 hours." I don't know why, but I really want to see the quality of the results of this method when using larger training sets. So my question is the following: will such method benefit from a distributed version (mapreduce) ? is such thing already available ? is it interesting to the Mahout project in general ? I started to document about it and it seems to require some big linear system solving.
