Journal article

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
Department
Physique
Language
  • English
Classification
Physics
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/301071
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