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Optimizing positional scoring rules for rank aggregation

Caragiannis, Ioannis and Chatzigeorgiou, Xenophon and Krimpas, George A and Voudouris, Alexandros A (2019) 'Optimizing positional scoring rules for rank aggregation.' Artificial Intelligence, 267. 58 - 77. ISSN 0004-3702

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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.

Item Type: Article
Uncontrolled Keywords: Computational social choice, Positional scoring voting rules, Crowdsourcing, Learning and voting, Preference learning, Approximation algorithms
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 20 May 2020 11:46
Last Modified: 20 May 2020 11:52

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