Ranking a Set of Objects using Heterogeneous Workers: QUITE an Easy Problem
This work addresses ranking challenges in crowdsourcing systems where workers have different skill levels, offering an incremental improvement over prior methods.
The paper tackles the problem of ranking objects from noisy pairwise comparisons by heterogeneous workers with varying reliability, proposing the QUITE algorithm to jointly estimate worker reliabilities and object qualities. It demonstrates QUITE's performance against existing algorithms in various scenarios and shows its adaptability.
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. More specifically, we assume that objects are endowed with intrinsic qualities and that the probability with which an object is preferred to another depends both on the difference between the qualities of the two competitors and on the reliability of the worker. We propose QUITE, a non-adaptive ranking algorithm that jointly estimates workers' reliabilities and qualities of objects. Performance of QUITE is compared in different scenarios against previously proposed algorithms. Finally, we show how QUITE can be naturally made adaptive.