Finding a Collective Set of Items: From Proportional Multirepresentation to Group Recommendation
Abstract
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 items that maximize the total derived utility of all the agents (i.e., in our example we are to pick 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.
Keywords
Cite
@article{arxiv.1402.3044,
title = {Finding a Collective Set of Items: From Proportional Multirepresentation to Group Recommendation},
author = {Piotr Skowron and Piotr Faliszewski and Jerome Lang},
journal= {arXiv preprint arXiv:1402.3044},
year = {2016}
}