Online Matching and Ad Allocation
By Aranyak Mehta. Foundations and Trends® in Theoretical Computer Science, 2013
489 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/b97895be87dc2292db615adcce453ce31ded38e0
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Richard S. Zemel
- Kevin Swersky
- Han-Jia Ye
- Elliot Creager
- De-chuan Zhan
- Omer Ben-Porat
- Craig Boutilier
- Bolin Ding
In Computational Theory and Mathematics
- Kevin Swersky
- Richard S. Zemel
- Omer Ben-Porat
- Craig Boutilier
- Bolin Ding
- Martin Mladenov
- Elliot Creager
- Yongxin Tong
Citations in the corpus
- A Marketplace for Data: An Algorithmic Solution (2018, background): “An extensive survey on online matching, in the context of ad allocation, can be found in [27].”
- Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach (2020, methodology): “Adaptive online methods (e.g., as used in ad auctions (Mehta, 2013)) can exploit optimization techniques like ours to dynamically adjust the matching without the full complexity of RL. Incomplete Information: Our model assumes complete knowledge of both user utility and “interests” (via the query…”
- Online mobile Micro-Task Allocation in spatial crowdsourcing (2016, background): “The goal of the OMWBM problem is to maximize the total sum of the weights of the matched edges [30].”