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
On Thu, Mar 22, 2012 at 5:38 PM, Sean Owen <[email protected]> wrote:
> You're implementing userSimilarity(), but appear to be computing
> item-item similarity. Halfway through, you use the item IDs as user
> IDs. I can't see what this is intending to do as a result?
>
>
