Look at AggregateAndRecommendReducer, which is the final stage in the distributed item-based computation. Look at the end of the reduceBooleanData() / reduceNonBooleanData() methods. This is where the final results are output. You can just cause it to skip any items you don't care about.
I think you could do this check a little earlier, in a prior stage, and save some computation. But this simple change would definitely work, as a start. On Mon, Aug 22, 2011 at 4:05 PM, Varnit Khanna <[email protected]> wrote: > On Sat, Aug 20, 2011 at 2:31 AM, Sean Owen <[email protected]> wrote: >> The non-distributed code handles this case with Rescorers. There is no >> equivalent in the distributed implementation. >> >> However it's fairly easy to hack this into the code. You just need to >> modify the final stage where recommendations are computed to reject >> items that are not new enough. They will have been used for similarity >> calculations already, but then you can filter them out of >> recommendations here. > What do you mean by "final stage"? Did you mean have a post process > which rejects old items produced by RecommenderJob or modify one of > steps in RecommenderJob? If you meant the latter can you provide any > documentation? > > Thanks > -varnit >
