I think that's certainly true for item-based recommenders (and item-item similarity). But isn't it a different story for user-user similarity? In the example below, "novel1" and "novel1-copy" are indeed still separate items--but won't they be separate items that produce duplicative forces (and thus "weighting") in terms of the user-user similarity between user1 and user?
I do realize that inflating the size of one's dataset in this way might lead to other problems. But setting that aside for now, I'd like to understand whether or not it would produce this kind of weighting effect for user-user similarities. Thanks, Jamey On Thu, Nov 17, 2011 at 10:59 AM, Sean Owen <[email protected]> wrote: > 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 > > > > > > > > > >
