Collaborative filtering based on multi-channel diffusion

Shang, Ming-Sheng; Jin, Ci-Hang; Zhou, Tao; Zhang, Yi-Cheng

doi:10.1016/j.physa.2009.08.011

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Journal article

Collaborative filtering based on multi-channel diffusion

Shang, Ming-Sheng Lab of Information Economy and Internet Research, University of Electronic Science and Technology, Chengdu, China
Jin, Ci-Hang Department of Physics, University of Fribourg, Switzerland
Zhou, Tao Department of Physics, University of Fribourg, Switzerland - Department of Modern Physics and Nonlinear Science Center, University of Science and Technology of China, Hefei, China
Zhang, Yi-Cheng Lab of Information Economy and Internet Research, University of Electronic Science and Technology, Chengdu, China - Department of Physics, University of Fribourg, Switzerland

15.08.2009

Published in:

Physica A: Statistical Mechanics and its Applications. - 2009, vol. 388, no. 23, p. 4867-4871

English 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.

Faculty

Faculté des sciences et de médecine

Department

Département de Physique

Language