Finding a Collective Set of Items: From Proportional Multirepresentation to Group Recommendation
Skowron, Piotr; Faliszewski, Piotr; Lang, Jérôme (2015), Finding a Collective Set of Items: From Proportional Multirepresentation to Group Recommendation, in Bonet, Blai; Koenig, Sven, Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI 2015), AAAI Press : Palo Alto (USA), p. 2131-2137
TypeCommunication / Conférence
Conference title29th AAAI Conference on Artificial Intelligence (AAAI 2015)
Conference cityAustin, Texas
Conference countryUnited States
Book titleProceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI 2015)
Book authorBonet, Blai; Koenig, Sven
Number of pages4331
MetadataShow full item record
Department of Automatics [AGH-UST]
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
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.
Subjects / Keywordssocial choice
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