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. Sean On Fri, Aug 19, 2011 at 8:39 PM, Bala Rajagopal <[email protected]> wrote: > > Hi, > My team is working on building a recommendation system to recommend items for > the following use cases:1. Based on User similarity (using > org.apache.mahout.cf.taste.hadoop.item.RecommenderJob as the Base)2. Based on > item similarity > The part where it gets tricky is that we have a temporal restriction on our > items (they are valid only for x days). So in the ideal case, the recommender > should/can use the rating information on all our historical items, but will > never recommend any items that are not temporally available. Based on the > historical rating information, we need the list of best matches from the > temporally available items. > Apart from ideas that involve any pre/post processing activities to filter > temporally invalid item recommendations, we were reaching out to find if > somebody out here has ever dealt with a similar requirement and has found an > easier solution to deal with this edge case. > Any piece of advice, word of caution or streak of brilliance is more than > welcome. > Thanks in advance. > Bala
