Thanks, Sean. We'll look into that. For user-based recommenders (or even just calculating UserSimilarity), would it have the desired effect if we added multiple "virtual" preference data points for the "real" items that we wished to more heavily weight? For example, if our "real" preference data were:
user1:novel1:3star user1:story1:4star user2:novel1:1star user2:story1:3star Would transforming it into this have the desired weighting effect (as long as we filtered out the "copy" items in any actual recommendations)? user1:novel1:3star user1:novel1-copy1:3star user1:story1:4star user2:novel1:1star user2:novel1-copy1:1star user2:story1:3star The hope would be that "novel1" would now have twice the weighting as "story1" in determining the similarity of these two users. Thanks, Jamey On Thu, Nov 17, 2011 at 10:29 AM, Sean Owen <[email protected]> wrote: > Not directly, but you could modify an item-based recommender to do so. > Where it uses an item-item similarity as a weight in a weighted average, > you could modify the weight however you like depending on the types of the > two items. > > On Thu, Nov 17, 2011 at 5:16 PM, Jamey Wood <[email protected]> wrote: > > > Is there some way to weight particular preferences within Mahout? For > > example, suppose you were creating some kind of literature recommender > that > > uses a 5-star preference scale. If you wanted to give double the > weighting > > to preferences for novels versus preferences for short stories, what > would > > be the best way to do it? > > > > Thanks, > > Jamey > > >
