Yes, but you can't use it as both things at once. I meant that you swap them at the broadest level -- at your original input. So all "items" are really users and vice versa. At the least you need two separate implementations, encapsulating two different notions of similarity.
Similarity is item-item or user-user, not item-item. It makes some sense to implement item-item similarity based on tags, so the first half of the method looks OK (excepting I'd expect you to implement itemSimilarity()). I think the other half makes more sense if you are calling getUsersForItem() -- input is item, output are users. As for the final line -- my original comment stands, though it's right for a wrong reason. You are not combining two distances here. You're combining a similarity value and a distance (right? proximity is a distance function?) and that's definitely not right. They go opposite ways: big distance means small similarity. If you handle two similarities, the simple thing that is in the ballpark on theoretically sound is to take their product. On Thu, Mar 22, 2012 at 9:48 PM, Ahmed Abdeen Hamed <[email protected]> wrote: > You are correct. In a previous post, I inquired about the use of > TreeClusteringRecommender which is based upon a UserSimilarity metrix. My > question was whether I can use it for ItemSimialrity, and your answer was > yes, just feed the itemID as a userID and vice versa and that's what I am > doing in it the method. This is what this code is doing > > The purpose of this method is to derive a similarity that is based on item > attributes (name, brand, category) in addition to what the loglikelihood > offers, so I am guaranteed to be getting recommendations for items such as > ("The Matrix", and "The Matrix Reloaded") if they never co-occur in the data > model. This is why I need to merge to the two scores somehow. > > Thanks again! > Ahmed
