Collaborative filtering based on multi-channel diffusion (0906.1148v1)

Abstract: 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.