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
Department
Physique
Language
  • English
Classification
Physics
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/300854
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