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 et de médecine
- Department
- Département de Physique
- Language
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- English
- Classification
- Physics
- License
- License undefined
- Identifiers
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- RERO DOC 17024
- DOI 10.1016/j.physa.2009.08.011
- Persistent URL
- https://folia.unifr.ch/unifr/documents/301495
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