Recommending Deployment Strategies for Collaborative Tasks

Dong Wei, Senjuti Basu Roy, Sihem Amer-Yahia

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Our work contributes to aiding requesters in deploying collaborative tasks in crowdsourcing. We initiate the study of recommending deployment strategies for collaborative tasks to requesters that are consistent with deployment parameters they desire: a lower-bound on the quality of the crowd contribution, an upper-bound on the latency of task completion, and an upper-bound on the cost incurred by paying workers. A deployment strategy is a choice of value for three dimensions: Structure (whether to solicit the workforce sequentially or simultaneously), Organization (to organize it collaboratively or independently), and Style (to rely solely on the crowd or to combine it with machine algorithms). We propose StratRec, an optimization-driven middle layer that recommends deployment strategies and alternative deployment parameters to requesters by accounting for worker availability. Our solutions are grounded in discrete optimization and computational geometry techniques that produce results with theoretical guarantees. We present extensive experiments on Amazon Mechanical Turk, and conduct synthetic experiments to validate the qualitative and scalability aspects of StratRec.

Original languageEnglish (US)
Title of host publicationSIGMOD 2020 - Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data
PublisherAssociation for Computing Machinery
Pages3-17
Number of pages15
ISBN (Electronic)9781450367356
DOIs
StatePublished - Jun 14 2020
Event2020 ACM SIGMOD International Conference on Management of Data, SIGMOD 2020 - Portland, United States
Duration: Jun 14 2020Jun 19 2020

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078

Conference

Conference2020 ACM SIGMOD International Conference on Management of Data, SIGMOD 2020
CountryUnited States
CityPortland
Period6/14/206/19/20

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

Keywords

  • approximation algorithm
  • computational geometry
  • crowdsourcing
  • deployment strategy recommendation

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