It's a good question. The bigger question here is, how do you create a weighted average when weights can be negative? That leads to wacky results like predicting ratings of -5 when ratings range from 1 to 5.
My fix was to make all weights nonnegative in this way. If you ignore items with similarity 0, what would you do with items with negative similarity? You could ignore them I suppose; it loses some key information, but might be OK. It also presupposes that similarity 0 means no resemblance at all; that's not necessarily what 0 means for similarity -- at least in the context of this framework. While it means no resemblance in the case of similarities built on things like the Pearson correlation, it doesn't for other metrics. Sean On Mon, Feb 22, 2010 at 12:54 PM, Tamas Jambor <[email protected]> wrote: > hi, > > Just wondering how you justify that you add +1 to the correlation, when you > calculate the score for the recommendation. > so that items which are not correlated constitute to the score. I think this > biases the recommender towards the mean of the ratings of the target users > (for item based), > > Tamas > > >
