Recommending Deployment Strategies for Collaborative Tasks

Document Type

Conference Proceeding

Publication Date

6-14-2020

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.

Identifier

85086259307 (Scopus)

ISBN

[9781450367356]

Publication Title

Proceedings of the ACM SIGMOD International Conference on Management of Data

External Full Text Location

https://doi.org/10.1145/3318464.3389719

ISSN

07308078

First Page

3

Last Page

17

Grant

N000141812838

Fund Ref

National Science Foundation

This document is currently not available here.

Share

COinS