Yeah I'm still looking for one matrix, I just need to be able to prune it based on data that lives outside of the model. This should work.
Thanks. Nick On Tue, Dec 27, 2011 at 5:30 PM, Sean Owen <[email protected]> wrote: > Yes, the rows of the user-item matrix exist in > AggregateAndRecommendReducer, when they reach writeRecommendedItems(). > This is where it's filtered. > > I'm not sure if this will do what you want depend ing on what you mean > by different dimensions. This is all still computing one user-item > matrix for one set of 'prefs'. There aren't any other dimensions of > anything hiding in here. > > On Tue, Dec 27, 2011 at 2:18 PM, Nick Jordan <[email protected]> wrote: > > I've been playing with RecommenderJob for the last couple of days and > have > > it successfully running on my data set. > > > > The one question I have remaining is with the output. While it's > possible > > to define the min and max number of recommendations, what I'd really like > > is the entire matrix of [users][items] with the recommendation score (if > > any). The problem I'm trying to solve here is that there are various > other > > dimensions of my data that I'd like to be able to filter by. If I just > > take the top n recommendations, none of them may meet the filter > criteria. > > > > I suppose one option would be to run a separate job for each dimension, > but > > I lose the benefit of recommendations outside of that then limited data > set. > > > > Is the entire recommendation matrix generated in one of the map-reduce > jobs > > prior to the final one that generates the output? Am I missing an easy > > solution to this? > > > > Thanks in advance. > > > > ~N >
