I committed MAHOUT-321, which changes how similarities are used as weights in the calculations. Similarities are no longer shifted to the range [0,2] from [-1,1]. This requires a little more care to rationalize, in order to not produce predictions that are way out of range.
It also copies logic into the user-based recommender from item-based recommender, which will ignore any estimate based on only one user-user similarity. The reasoning is that in a weighted average over one data point, the weight is irrelevant, and that's not intuitive or good. It will change predictions. There are theoretical reasons it will improve accuracy, and some empirical evidence too. However I think this is a change worth continuing to play with and evaluate in practice to ensure it's really helping. Sean
