If you are making recommendations, then there is no item1 as input. You're only given user1. This is true in user-based or item-based recommendation.
You are right that if we just wanted to predict one rating, you would have user1 and item1 as input. All of the existing recommender implementations actually can do this through the estimatePreference() method. None work by computing a neighborhood of items, since that's not suitable to make recommendations. However the item-based recommender in the framework can tell you the items most similar to a given item. You could use that on your own to perform the computation you are thinking of. You might run into the following issue: in a sparse data set, user1 might not have rated anything in the immediate neighborhood of item1. Sean On Sat, Feb 20, 2010 at 12:36 AM, jamborta <[email protected]> wrote: > > if we want to make a prediction for user1,item1 than it's the neighbourhood > of item1. > > As you mentioned earlier I'm turning the classic recommender problem on its > side. maybe i don't understand the problem exactly, but most of the papers I > read think along this line.
