Information filtering based on transferring similarity
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Sun, Duo
Department of Modern Physics and Nonlinear Science Center, University of Science and Technology of China, Hefe, China
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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
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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
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Liu, Run-Ran
Department of Modern Physics and Nonlinear Science Center, University of Science and Technology of China, Hefe, China
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Jia, Chun-Xiao
Department of Modern Physics and Nonlinear Science Center, University of Science and Technology of China, Hefe, China
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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
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Published in:
- Physical Review E. - 2009, vol. 80, no. 1, p. 017101
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.
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Faculty
- Faculté des sciences et de médecine
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Department
- Département de Physique
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Language
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Classification
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Physics
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License
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License undefined
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Identifiers
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Persistent URL
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https://folia.unifr.ch/unifr/documents/301206
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