  In the last years, due to the great diffusion of e-commerce, online rating
platforms quickly became a common tool for purchase recommendations. However,
instruments for their analysis did not evolve at the same speed. Indeed,
interesting information about users' habits and tastes can be recovered just
considering the bipartite network of users and products, in which links have
different weights due to the score assigned to items. With respect to other
weighted bipartite networks, in these systems we observe a maximum possible
weight per link, that limits the variability of the outcomes. In the present
article we propose an entropy-based randomisation of (bipartite) rating
networks by extending the Configuration Model framework: the randomised network
satisfies the constraints of the degree per rating, i.e. the number of given
ratings received by the specified product or assigned by the single user. We
first show that such a null model is able to reproduce several non-trivial
features of the real network better than other null models. Then, using it as a
benchmark, we project the information contained in the real system on one of
the layers, showing, for instance, the division in communities of music albums
due to the taste of customers, or, in movies due the audience.
