  In this paper, by applying a diffusion process, we propose a new index to
quantify the similarity between two users in a user-object bipartite graph. To
deal with the discrete ratings on objects, we use a multi-channel
representation where each object is mapped to several channels with the number
of channels being equal to the number of different ratings. Each channel
represents a certain rating and a user having voted an object will be connected
to the channel corresponding to the rating. Diffusion process taking place on
such a user-channel bipartite graph gives a new similarity measure of user
pairs, which is further demonstrated to be more accurate than the classical
Pearson correlation coefficient under the standard collaborative filtering
framework.
