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
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    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
Department
Physique
Language
  • English
Classification
Physics
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/301495
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