Information filtering via Iterative Refinement
P. Laureti, L. Moret, Y. -C. Zhang, Y. -K. Yu
Abstract
With the explosive growth of accessible information, expecially on the Internet, evaluation-based filtering has become a crucial task. Various systems have been devised aiming to sort through large volumes of information and select what is likely to be more relevant. In this letter we analyse a new ranking method, where the reputation of information providers is determined self-consistently.
Create a lesson
Related papers
Multi-fidelity Monte Carlo estimation of floor response spectra under combined seismic and structural parameter uncertainties
Nils Baillie, Baptiste Kerleguer, Cyril Feau et al.
Parameter inference from a non-stationary unknown process using statistical feature-based slow feature analysis
Kieran S. Owens, Masako Tamaki, Ben D. Fulcher
A Probability Model for Pentagonal Prism Dice Rolls
Paul R. Hurst, J. Naleo Hyde
Geometry-native machine learning reconstruction of DSMC moment fields with support monitoring
Ehsan Roohi
Multivariate amplitude analysis of the cascade particle decays based on the Nearest Neighbors fitting
I. V. Yeletskikh, A. O. Vasyukov
The geometry of uncertainty decomposition in profile-likelihood fits
Rafael Coelho Lopes de Sá