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 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.
Cite
Text
Skowron et al. "Finding a Collective Set of Items: From Proportional Multirepresentation to Group Recommendation." AAAI Conference on Artificial Intelligence, 2015. doi:10.1609/AAAI.V29I1.9431Markdown
[Skowron et al. "Finding a Collective Set of Items: From Proportional Multirepresentation to Group Recommendation." AAAI Conference on Artificial Intelligence, 2015.](https://mlanthology.org/aaai/2015/skowron2015aaai-finding/) doi:10.1609/AAAI.V29I1.9431BibTeX
@inproceedings{skowron2015aaai-finding,
title = {{Finding a Collective Set of Items: From Proportional Multirepresentation to Group Recommendation}},
author = {Skowron, Piotr Krzysztof and Faliszewski, Piotr and Lang, Jérôme},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2015},
pages = {2131-2137},
doi = {10.1609/AAAI.V29I1.9431},
url = {https://mlanthology.org/aaai/2015/skowron2015aaai-finding/}
}