article

Online Matching and Ad Allocation

Author:

Aranyak MehtaAuthors Info & Claims

Foundations and Trends® in Theoretical Computer Science, Volume 8, Issue 4

Pages 265 - 368

https://doi.org/10.1561/0400000057

Published: 17 October 2013 Publication History

Abstract

Matching is a classic problem with a rich history and a significant impact, both on the theory of algorithms and in practice. Recently there has been a surge of interest in the online version of matching and its generalizations, due to the important new application domain of Internet advertising. The theory of online matching and allocation has played a critical role in designing algorithms for ad allocation. This monograph surveys the key problems, models and algorithms from online matchings, as well as their implication in the practice of ad allocation. The goal is to provide a classification of the problems in this area, an introduction into the techniques used, a glimpse into the practical impact, and to provide direction in terms of open questions. Matching continues to find core applications in diverse domains, and the advent of massive online and streaming data emphasizes the future applicability of the algorithms and techniques surveyed here.

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cover image Foundations and Trends® in Theoretical Computer Science

Foundations and Trends® in Theoretical Computer Science Volume 8, Issue 4

October 2013

106 pages

ISSN:1551-305X

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Now Publishers Inc.

Hanover, MA, United States

Publication History

Published: 17 October 2013

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Spaeh FEne AOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Online ad allocation with predictionsProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3666878(17265-17295)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3666878
Li PYang JRen SKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Learning for edge-weighted online bipartite matching with robustness guaranteesProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619244(20276-20295)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619244
Wu RBao WGe LElkind E(2023)Online task assignment with controllable processing timeProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/607(5466-5474)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/607
Hosseini HHuang ZIgarashi AShah NWilliams BChen YNeville J(2023)Class fairness in online matchingProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i5.25704(5673-5680)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1609/aaai.v37i5.25704
Esmaeili SDuppala SCheng DNanda VSrinivasan ADickerson JWilliams BChen YNeville J(2023)Rawlsian fairness in online bipartite matchingProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i5.25698(5624-5632)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1609/aaai.v37i5.25698
Goyal VUdwani R(2023)Online Matching with Stochastic RewardsOperations Research10.1287/opre.2022.234571:2(563-580)Online publication date: 1-Mar-2023
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Ashlagi IBurq MDutta CJaillet PSaberi ASholley C(2023)Edge-Weighted Online Windowed MatchingMathematics of Operations Research10.1287/moor.2022.128948:2(999-1016)Online publication date: 1-May-2023
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