Information filtering based on transferring similarity.
In 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.
Sun
Duo
D
Department of Modern Physics and Nonlinear Science Center, University of Science and Technology of China, Hefei Anhui 230026, People's Republic of China.
Zhou
Tao
T
Liu
Jian-Guo
JG
Liu
Run-Ran
RR
Jia
Chun-Xiao
CX
Wang
Bing-Hong
BH