I don't think that would quite help, since novel1 and its copy are then different items, and not somehow combining forces in the final calculation.
On Thu, Nov 17, 2011 at 5:50 PM, Jamey Wood <[email protected]> wrote: > 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 > > > > > >
