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hal.structure.identifierOxford University
dc.contributor.authorSkowron, Piotr
hal.structure.identifierDepartment of Automatics [AGH-UST]
dc.contributor.authorFaliszewski, Piotr
hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorLang, Jérôme
dc.date.accessioned2017-01-18T13:09:22Z
dc.date.available2017-01-18T13:09:22Z
dc.date.issued2016
dc.identifier.issn0004-3702
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/16189
dc.language.isoenen
dc.subjectProportional representationen
dc.subjectOrdered weighted averageen
dc.subjectChamberlin–Courant ruleen
dc.subjectComputational complexityen
dc.subjectComputational social choiceen
dc.subjectApproximationen
dc.subjectElectionsen
dc.subjectVotingen
dc.subject.ddc006.3en
dc.titleFinding a collective set of items: From proportional multirepresentation to group recommendationen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenWe 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.en
dc.relation.isversionofjnlnameArtificial Intelligence
dc.relation.isversionofjnlissue241en
dc.relation.isversionofjnldate2016-12
dc.relation.isversionofjnlpages191-216en
dc.relation.isversionofdoi10.1016/j.artint.2016.09.003en
dc.relation.isversionofjnlpublisherElsevieren
dc.subject.ddclabelIntelligence artificielleen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenonen
dc.description.halcandidateouien
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewedouien
dc.relation.Isversionofjnlpeerreviewedouien
dc.date.updated2017-01-18T13:00:09Z
hal.identifierhal-01439247*
hal.version1*
hal.author.functionaut
hal.author.functionaut
hal.author.functionaut


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