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Minimising the rank aggregation error

Minimising the rank aggregation error
Minimising the rank aggregation error
Rank aggregation is the problem of generating an overall ranking from a set of individual votes. The aim in doing so is to produce a ranking which is as close as possible to the (unknown) correct ranking for a given distance measure such as the Kendall-tau distance. The challenge is that votes are often both noisy and incomplete. Existing work has largely focused on finding the most likely ranking for a particular noise model (such as Mallows'). Instead, here we focus on minimising the error, i.e., the expected distance between the aggregated ranking and the true underlying one. Specifically, we show that the two objectives result in different rankings, and that these differences become especially significant when many votes are missing. Furthermore, we show how to compute local improvements on existing rankings to reduce the expected error. Finally, we run extensive experiments on both synthetic and real data to compare different aggregation rules. In particular, a surprising result is that for votes generated according to the Mallows' model, Copeland often outperforms Kemeny optimal, despite the latter being the maximum likelihood estimator.
De Weerdt, Mathijs
2036d48e-e5b5-4d30-b5e5-af238eebf6ef
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
Stein, Sebastian
fb227373-7242-4982-b84b-90bc79617a50
De Weerdt, Mathijs
2036d48e-e5b5-4d30-b5e5-af238eebf6ef
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
Stein, Sebastian
fb227373-7242-4982-b84b-90bc79617a50

De Weerdt, Mathijs, Gerding, Enrico and Stein, Sebastian (2016) Minimising the rank aggregation error. Proceedings of the 15th International Conference on Autonomous Agents and Multi-Agent Systems, Singapore. 09 - 13 May 2016. 2 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

Rank aggregation is the problem of generating an overall ranking from a set of individual votes. The aim in doing so is to produce a ranking which is as close as possible to the (unknown) correct ranking for a given distance measure such as the Kendall-tau distance. The challenge is that votes are often both noisy and incomplete. Existing work has largely focused on finding the most likely ranking for a particular noise model (such as Mallows'). Instead, here we focus on minimising the error, i.e., the expected distance between the aggregated ranking and the true underlying one. Specifically, we show that the two objectives result in different rankings, and that these differences become especially significant when many votes are missing. Furthermore, we show how to compute local improvements on existing rankings to reduce the expected error. Finally, we run extensive experiments on both synthetic and real data to compare different aggregation rules. In particular, a surprising result is that for votes generated according to the Mallows' model, Copeland often outperforms Kemeny optimal, despite the latter being the maximum likelihood estimator.

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More information

Accepted/In Press date: 25 January 2016
e-pub ahead of print date: 9 May 2016
Venue - Dates: Proceedings of the 15th International Conference on Autonomous Agents and Multi-Agent Systems, Singapore, 2016-05-09 - 2016-05-13
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 390801
URI: http://eprints.soton.ac.uk/id/eprint/390801
PURE UUID: eb6131c4-faa8-4230-82fc-f4cf0ecc16d0
ORCID for Enrico Gerding: ORCID iD orcid.org/0000-0001-7200-552X

Catalogue record

Date deposited: 05 Apr 2016 02:17
Last modified: 17 Dec 2019 01:46

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Contributors

Author: Mathijs De Weerdt
Author: Enrico Gerding ORCID iD
Author: Sebastian Stein

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