Journal article

Information filtering via self-consistent refinement

  • Ren, Jie Department of Physics, University of Fribourg, Switzerland - Department of Physics and Centre for Computational Science and Engineering, National University of Singapore, Republic of Singapore
  • Zhou, Tao Department of Physics, University of Fribourg, Switzerland - Department of Modern Physics, University of Science and Technology of China, Hefei, PRC - Information Economy and Internet Research Laboratory, University of Electronic Science and Technology of China, Chengdu, PRC
  • Zhang, Yi-Cheng Department of Physics, University of Fribourg, Switzerland - Information Economy and Internet Research Laboratory, University of Electronic Science and Technology of China, Chengdu, PRC
    30.05.2008
Published in:
  • Europhysics Letters. - 2008, vol. 82, no. 5, p. 58007
English Recommender systems are significant to help people deal with the world of information explosion and overload. In this letter, we develop a general framework named self-consistent refinement and implement it by embedding two representative recommendation algorithms: similarity-based and spectrum-based methods. Numerical simulations on a benchmark data set demonstrate that the present method converges fast and can provide quite better performance than the standard methods.
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/300854
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