Incentive and Reputation Mechanisms for Online Crowdsourcing Systems
Refereed conference paper presented and published in conference proceedings


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AbstractNowadays, online crowdsourcing services are quite common such as Amazon Mechanical Turk and Google Helpouts. For such online services, it is important to attract "workers" to provide high-quality solutions to the "tasks" outsourced by "requesters". We present a unified study of incentive and reputation mechanisms for online crowdsourcing systems. We first design an mechanism to incentivize workers provide their maximum effort, which allows multiple workers to solve a task, splits the reward among workers based on requester evaluations of the solution quality. We design a reputation mechanism, which ensures that low-skilled workers do not provide low-quality solutions by tracking workers' historical contributions, and penalizing those workers having poor reputation. We show that our incentive and reputation mechanisms are robust against human biases in solution quality evaluation.
All Author(s) ListXie H, Lui JCS, Towsley D
Name of Conference23rd IEEE/ACM International Symposium on Quality of Service (IWQoS)
Start Date of Conference13/06/2015
End Date of Conference20/06/2015
Place of ConferencePortland
Country/Region of ConferenceUnited States of America
Detailed descriptionorganized by IEEE/ACM. This conference was considered as a tier-A conference as classified by the external visiting committee in FoE in 2011.
Year2015
Month1
Day1
PublisherIEEE
Pages207 - 212
eISBN978-1-4673-7113-1
LanguagesEnglish-United Kingdom
Keywordscrowdsourcing; incentive; reputation
Web of Science Subject CategoriesComputer Science; Computer Science, Theory & Methods; Engineering; Engineering, Electrical & Electronic

Last updated on 2020-26-05 at 01:46