Journal
ARTIFICIAL INTELLIGENCE
Volume 267, Issue -, Pages 58-77Publisher
ELSEVIER
DOI: 10.1016/j.artint.2018.11.001
Keywords
Computational social choice; Positional scoring voting rules; Crowdsourcing; Learning and voting; Preference learning; Approximation algorithms
Categories
Funding
- University of Patras [E.114]
- Onassis Foundation
- European Research Council (ERC) [639945]
Ask authors/readers for more resources
Nowadays, several crowdsourcing projects exploit social choice methods for computing an aggregate ranking of alternatives given individual rankings provided by workers. Motivated by such systems, we consider a setting where each worker is asked to rank a fixed (small) number of alternatives and, then, a positional scoring rule is used to compute the aggregate ranking. Among the apparently infinite such rules, what is the best one to use? To answer this question, we assume that we have partial access to an underlying true ranking. Then, the important optimization problem to be solved is to compute the positional scoring rule whose outcome, when applied to the profile of individual rankings, is as close as possible to the part of the underlying true ranking we know. We study this fundamental problem from a theoretical viewpoint and present positive and negative complexity results and, furthermore, complement our theoretical findings with experiments on real-world and synthetic data. (C) 2018 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available