Hi Sean, the prediction computation for boolean data is done in AggregateAndRecommendReducer.reduceBooleanData()
It computes *all* possible items to recommend for the current user and writes out only the n first after that, with n being the number specified in the parameter --numRecommendations given to RecommenderJob. Can you point me to the code where the non-distributed code handles the problem of ranking them? We could certainly emulate that behaviour in the distributed code too. --sebastian Am 26.11.2010 19:35, schrieb Sean Owen: > But is it then ranking the recommendations by the estimated pref? If > it's always 1, then the ordering is not meaningful. > > Maybe it is, I just haven't looked at your changes in much detail > since you made them although it looked broadly correct and proper. > > On Fri, Nov 26, 2010 at 6:33 PM, Sebastian Schelter <[email protected]> wrote: > >> If all ratings have value 1 (cause we use boolean data) the result of >> the Predicition can also only be 1. >>
