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(ICWS 2020) Reducing the Cost of Aggregation in Crowdsourcing

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Abstract: "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."

Authors: Loïc Hélouët (INRIA Rennes, France); Rituraj Singh (Univ Rennes/INRIA/IRISA, France); Zoltan Miklos (University Rennes 1, France)

Email: loic.helouet@inria.fr, rituraj.singh@irisa.fr, zoltan.miklos@irisa.fr

Who can view: Everyone
Webinar Price: Free
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Rituraj Singh is a PhD student at IRISA/INRIA registered at Universite de Rennes 1, France. His working thesis is "Data centric workflows for crowdsourcing applications" under the supervision of Dr. Loic Helouet, Researcher, INRIA and Dr. Zoltan Mikols, Assistant Professor at Universite de Rennes 1. He is a part of DRUID & SUMO team. Before joining here, he was working as a Researcher at Tata Consultancy Services (TCS) Research & Innovation Lab, Kolkata, India. He graduated from the Computer Science department of IIT Patna in June, 2015.  His area of interest are Data Centric Systems and Workflows,Crowdsourcing, Machine Learning,, Databases, Data Mining and Probabilistic Modelling of Systems.
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