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Finding a collective set of items: From proportional multirepresentation to group recommendation

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Date
2016
Dewey
Intelligence artificielle
Sujet
Proportional representation; Ordered weighted average; Chamberlin–Courant rule; Computational complexity; Computational social choice; Approximation; Elections; Voting
Journal issue
Artificial Intelligence
Number
241
Publication date
12-2016
Article pages
191-216
Publisher
Elsevier
DOI
http://dx.doi.org/10.1016/j.artint.2016.09.003
URI
https://basepub.dauphine.fr/handle/123456789/16189
Collections
  • LAMSADE : Publications
Metadata
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Author
Skowron, Piotr
98120 Oxford University
Faliszewski, Piotr
200767 Department of Automatics [AGH-UST]
Lang, Jérôme
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Type
Article accepté pour publication ou publié
Abstract (EN)
We consider the following problem: There is a set of items (e.g., movies) and a group of agents (e.g., passengers on a plane); each agent has some intrinsic utility for each of the items. Our goal is to pick a set of K items that maximize the total derived utility of all the agents (i.e., in our example we are to pick K movies that we put on the plane's entertainment system). However, the actual utility that an agent derives from a given item is only a fraction of its intrinsic one, and this fraction depends on how the agent ranks the item among the chosen, available, ones. We provide a formal specification of the model and provide concrete examples and settings where it is applicable. We show that the problem is hard in general, but we show a number of tractability results for its natural special cases.

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