Yes, great point. It's bad if there's only one item that the user has rated that has any similarity to the item being predicted. According to even the 'corrected' formula, the similarity value doesn't even matter. It cancels out. That leads to the counter-intuitive possibility you highlight.
For that reason GenericItemBasedRecommender won't make a prediction in this situation. You could argue it's a hack but I feel it should be undefined in this situation. You could certainly throw out 3.2.1 entirely and think up something better, though I think with the two tweaks I've described here, its core logic is simple and remains sound. Sean On Thu, Feb 11, 2010 at 12:04 AM, Guohua Hao <[email protected]> wrote: > I think you brought up a good point as to dealing with negative > similarities, which I have not realized before. Here is my other thought. > Based on your example and the proposed method, we will get a predicted > rating of 5 in such case after normalization. This seems counter-intuitive > to me, since we know that these two items are very dissimilar (actually > opposite correlated), a predicted rating close to 1 will be more intuitive > to me. Maybe we need to think more about the expression in section 3.2.1 of > that paper.
