Reducing the Cost of Aggregation in Crowdsourcing - Archive ouverte HAL Access content directly
Preprints, Working Papers, ... Year :

Reducing the Cost of Aggregation in Crowdsourcing

(1) , (2) , (1)


Crowdsourcing is a way to solve problems that need human contribution. Crowdsourcing platforms distribute replicated tasks to workers, pay them for their contribution, and aggregate answers to produce a reliable conclusion. A fundamental problem is to infer a correct answer from the set of returned results. Another challenge is to obtain a reliable answer at a reasonable cost: unlimited budget allows hiring experts or large pools of workers for each task but a limited budget forces to use resources at best. This paper considers crowdsourcing of simple boolean tasks. We first define a probabilistic inference technique, that considers difficulty of tasks and expertise of workers when aggregating answers. We then propose CrowdInc, a greedy algorithm that reduce the cost needed to reach a consensual answer. CrowdInc distributes resources dynamically to tasks according to their difficulty. We show on several benchmarks that CrowdInc achieves good accuracy, reduces costs, and we compare its performance to existing solutions.
Fichier principal
Vignette du fichier
Main.pdf (824.32 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-02397971 , version 1 (06-12-2019)


  • HAL Id : hal-02397971 , version 1


Rituraj Singh, Loïc Hélouët, Zoltán Miklós. Reducing the Cost of Aggregation in Crowdsourcing. 2019. ⟨hal-02397971⟩
86 View
266 Download


Gmail Facebook Twitter LinkedIn More