Personal recommendation via modified collaborative filtering
- Liu, Run-Ran Department of Modern Physics and Nonlinear Science Center, University of Science and Technology of China, Hefei Anhui, China
- Jia, Chun-Xiao Department of Modern Physics and Nonlinear Science Center, University of Science and Technology of China, Hefei Anhui, China
- Zhou, Tao Department of Modern Physics and Nonlinear Science Center, University of Science and Technology of China, Hefei Anhui, China - Department of Physics, University of Fribourg, Switzerland
- Sun, Duo Department of Modern Physics and Nonlinear Science Center, University of Science and Technology of China, Hefei Anhui, China
- Wang, Bing-Hong Department of Modern Physics and Nonlinear Science Center, University of Science and Technology of China, Hefei Anhui, China
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17.10.2008
Published in:
- Physica A: Statistical Mechanics and its Applications. - 2009, vol. 388, no. 4, p. 462-468
English
In this paper, we propose a novel method to compute the similarity between congeneric nodes in bipartite networks. Different from the standard cosine similarity, we take into account the influence of a node’s degree. Substituting this new definition of similarity for the standard cosine similarity, we propose a modified collaborative filtering (MCF). Based on a benchmark database, we demonstrate the great improvement of algorithmic accuracy for both user-based MCF and object-based MCF.
- 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 11359
- DOI 10.1016/j.physa.2008.10.010
- Persistent URL
- https://folia.unifr.ch/unifr/documents/301071
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