Selecting the top-quality item through crowd scoring

TL;DR

This paper develops and evaluates adaptive crowdsourcing algorithms to efficiently identify the highest-quality item from a large set, accounting for noisy and biased evaluations.

Contribution

It introduces a probabilistic model and proposes adaptive algorithms that improve the efficiency of selecting top-quality items in crowdsourcing scenarios.

Findings

Some algorithms achieve near-optimal performance

Adaptive algorithms outperform non-adaptive methods

Performance depends on system parameter settings

Abstract

We investigate crowdsourcing algorithms for finding the top-quality item within a large collection of objects with unknown intrinsic quality values. This is an important problem with many relevant applications, for example in networked recommendation systems. The core of the algorithms is that objects are distributed to crowd workers, who return a noisy and biased evaluation. All received evaluations are then combined, to identify the top-quality object. We first present a simple probabilistic model for the system under investigation. Then, we devise and study a class of efficient adaptive algorithms to assign in an effective way objects to workers. We compare the performance of several algorithms, which correspond to different choices of the design parameters/metrics. In the simulations we show that some of the algorithms achieve near optimal performance for a suitable setting of the system parameters.