Optimizing positional scoring rules for rank aggregation
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, the...
| Main Authors: | , , , |
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| פורמט: | Journal article |
| יצא לאור: |
Elsevier
2018
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| _version_ | 1826296972560039936 |
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| author | Caragiannis, I Chatzigeorgiou, X Krimpas, G Voudouris, A |
| author_facet | Caragiannis, I Chatzigeorgiou, X Krimpas, G Voudouris, A |
| author_sort | Caragiannis, I |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T04:24:32Z |
| format | Journal article |
| id | oxford-uuid:cc2a7d81-0d9c-4b9c-8324-0f72ebbb030a |
| institution | University of Oxford |
| last_indexed | 2024-03-07T04:24:32Z |
| publishDate | 2018 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:cc2a7d81-0d9c-4b9c-8324-0f72ebbb030a2022-03-27T07:20:03ZOptimizing positional scoring rules for rank aggregationJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:cc2a7d81-0d9c-4b9c-8324-0f72ebbb030aSymplectic Elements at OxfordElsevier2018Caragiannis, IChatzigeorgiou, XKrimpas, GVoudouris, ANowadays, 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. |
| spellingShingle | Caragiannis, I Chatzigeorgiou, X Krimpas, G Voudouris, A Optimizing positional scoring rules for rank aggregation |
| title | Optimizing positional scoring rules for rank aggregation |
| title_full | Optimizing positional scoring rules for rank aggregation |
| title_fullStr | Optimizing positional scoring rules for rank aggregation |
| title_full_unstemmed | Optimizing positional scoring rules for rank aggregation |
| title_short | Optimizing positional scoring rules for rank aggregation |
| title_sort | optimizing positional scoring rules for rank aggregation |
| work_keys_str_mv | AT caragiannisi optimizingpositionalscoringrulesforrankaggregation AT chatzigeorgioux optimizingpositionalscoringrulesforrankaggregation AT krimpasg optimizingpositionalscoringrulesforrankaggregation AT voudourisa optimizingpositionalscoringrulesforrankaggregation |