no code implementations • 3 Oct 2023 • Alessandro Nordio, Alberto Tarable, Emilio Leonardi
We focus on the problem of ranking $N$ objects starting from a set of noisy pairwise comparisons provided by a crowd of unequal workers, each worker being characterized by a specific degree of reliability, which reflects her ability to rank pairs of objects.
no code implementations • 26 Feb 2020 • Evgenia Christoforou, Alessandro Nordio, Alberto Tarable, Emilio Leonardi
We propose a class of non-adaptive ranking algorithms that rely on a least-squares optimization criterion for the estimation of qualities.
no code implementations • 23 Dec 2015 • Alessandro Nordio, Alberto Tarable, Emilio Leonardi, Marco Ajmone Marsan
We investigate crowdsourcing algorithms for finding the top-quality item within a large collection of objects with unknown intrinsic quality values.