User Response in Ad Auctions: An MDP Formulation of Long-term Revenue Optimization
Author:
Affiliation:
1. Yale University, New Haven, CT, USA
2. Google, Mountain View, CA, USA
3. Google, Mountain View, CA, Canada
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3589334.3645495
Reference42 articles.
1. Ad Auction Design and User Experience
2. Alekh Agarwal, Sham Kakade, and Lin F Yang. 2020. Model-based reinforcement learning with a generative model is minimax optimal. In Conference on Learning Theory. PMLR, 67--83.
3. Itai Ashlagi, Benjamin G Edelman, and Hoan Lee. 2013. Competing ad auctions. Harvard Business School NOM Unit Working Paper 10-055 (2013).
4. Position Auctions with Consumer Search
5. An Efficient Dynamic Mechanism
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