  We propose here two new recommendation methods, based on the appropriate
normalization of already existing similarity measures, and on the convex
combination of the recommendation scores derived from similarity between users
and between objects. We validate the proposed measures on three relevant data
sets, and we compare their performance with several recommendation systems
recently proposed in the literature. We show that the proposed similarity
measures allow to attain an improvement of performances of up to 20\% with
respect to existing non-parametric methods, and that the accuracy of a
recommendation can vary widely from one specific bipartite network to another,
which suggests that a careful choice of the most suitable method is highly
relevant for an effective recommendation on a given system. Finally, we studied
how an increasing presence of random links in the network affects the
recommendation scores, and we found that one of the two recommendation
algorithms introduced here can systematically outperform the others in noisy
data sets.
