Back
pdf
Journal article

Information filtering based on transferring similarity

  • Sun, Duo Department of Modern Physics and Nonlinear Science Center, University of Science and Technology of China, Hefe, China
  • Zhou, Tao Department of Modern Physics and Nonlinear Science Center, University of Science and Technology of China, Hefe, China - Department of Physics, University of Fribourg, Switzerland
  • Liu, Jian-Guo Department of Modern Physics and Nonlinear Science Center, University of Science and Technology of China, Hefe, China - Department of Physics, University of Fribourg, Switzerland
  • Liu, Run-Ran Department of Modern Physics and Nonlinear Science Center, University of Science and Technology of China, Hefe, China
  • Jia, Chun-Xiao Department of Modern Physics and Nonlinear Science Center, University of Science and Technology of China, Hefe, China
  • Wang, Bing-Hong Department of Modern Physics and Nonlinear Science Center, University of Science and Technology of China, Hefe, China - Research Center for Complex System Science, University of Shanghai for Science and Technology, Shanghai, China
Show more…
    06.07.2009
Published in:
  • Physical Review E. - 2009, vol. 80, no. 1, p. 017101
filtering theory
network theory (graphs)
prediction theory
series (mathematics)
statistics
English n this Brief Report, we propose an index of user similarity, namely, the transferring similarity, which involves all high-order similarities between users. Accordingly, we design a modified collaborative filtering algorithm, which provides remarkably higher accurate predictions than the standard collaborative filtering. More interestingly, we find that the algorithmic performance will approach its optimal value when the parameter, contained in the definition of transferring similarity, gets close to its critical value, before which the series expansion of transferring similarity is convergent and after which it is divergent. Our study is complementary to the one reported in [E. A. Leicht, P. Holme, and M. E. J. Newman, Phys. Rev. E 73, 026120 (2006)], and is relevant to the missing link prediction problem.
Faculty
Faculté des sciences et de médecine
Department
Département de Physique
Language
  • English
Classification
Physics
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/301206
Statistics
Document views: 35 File downloads:
  • zhou_ifb.pdf: 42