Preference Elicitation in Matching Markets Via Interviews: A Study of Offline Benchmarks

Rastegari, B., Goldberg, P. and Manlove, D. (2016) Preference Elicitation in Matching Markets Via Interviews: A Study of Offline Benchmarks. In: AAMAS 2016, Singapore, 9-13 May 2016, pp. 1393-1394. ISBN 9781450342391



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In this paper we study two-sided matching markets in which the participants do not fully know their preferences and need to go through some costly deliberation process in order to learn their preferences. We assume that such deliberations are carried out via interviews, thus the problem is to find a good strategy for interviews to be carried out in order to minimize their use, whilst leading to a stable matching. One way to evaluate the performance of an interview strategy is to compare it against a nave ïalgorithm that conducts all interviews. We argue however that a more meaningful comparison would be against an optimal offline algorithm that has access to agents' preference orderings under complete information. We show that, unless P=NP, no offline algorithm can compute the optimal interview strategy in polynomial time. If we are additionally aiming for a particular stable matching, we provide restricted settings under which efficient optimal offline algorithms exist.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Manlove, Professor David and Rastegari, Dr Baharak
Authors: Rastegari, B., Goldberg, P., and Manlove, D.
College/School:College of Science and Engineering > School of Computing Science
Copyright Holders:Copyright © 2016 ACM
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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