On Wed, Mar 3, 2010 at 1:45 PM, Tamas Jambor <[email protected]> wrote: > my problem is that using Person correlation with adjusted weighed average > prediction (i.e. not shifting the weights) results in a very bad RMSE, > especially for item-based (but I get a surprisingly good results for other > measures), so that surely something wrong with negative weights.
Yes, that alone is not sufficient -- you'd need to cap the estimated preference value at the minimum / maximum possible values. Otherwise the RMSE could be infinite, since the estimate can be arbitrarily large or small. My patch for issue 321 will address this, such that you can safely use Pearson. Or you can hack it into your own customized version. > however, just tried cosine similarity and EuclideanDistanceSimilarity for > item-based, they are OK in terms of RMSE (around 1.03 for both), although > EuclideanDistanceSimilarity performs a slightly better with other measures ( > e.g. NDCG, MAP, precision, etc). Euclidean distance similarity works because its weights aren't negative. Even if you aren't capping the estimates, you won't get such out-of-bounds estimates. (Uncentered) Cosine measure similarity works for the same reason, assuming your preference value range is nonnegative -- the cosine is never negative then. The real solution isn't really to use uncentered data, but to fully implement support for negative weights, which means capping.
