Well I think you could fit it inside some of the user-user similarities, yes. For a Pearson correlation, you could count important items twice or something, yes. I wouldn't do that by literally adding more items to the model as it creates other problems. It's possible; it may or may not have the type and magnitude of effect you're looking for but easy enough to try.
On Thu, Nov 17, 2011 at 6:25 PM, Jamey Wood <jamey.w...@gmail.com> wrote: > 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 <sro...@gmail.com> 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 <jamey.w...@gmail.com> > 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 <sro...@gmail.com> 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 <jamey.w...@gmail.com> > > > 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 > > > > > > > > > > > > > > >
