Reinforcement Mechanism Design for e-commerce-Reference-Cited by-同舟云学术

Reinforcement Mechanism Design for e-commerce

Published:2018 Issue: Volume: Page:
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Container-title:Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18
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Author:

Cai Qingpeng¹,Filos-Ratsikas Aris²,Tang Pingzhong¹,Zhang Yiwei³

Affiliation:

1. Tsinghua University, Beijing, China

2. University of Oxford, Oxford, United Kingdom

3. University of California, Berkeley, Berkeley, CA, USA

Funder

Tsinghua University Initiative Scienti c Research Grant

National Natural Science Foundation of China Grant

Alibaba Innovative Research program

China Youth 1000-talent program

ERC Advanced Grant

Publisher

ACM Press

Reference43 articles.

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2. Rajeev Agrawal . 1995. Sample mean based index policies by O (log n) regret for the multi-armed bandit problem. Advances in Applied Probability Vol. 27, 4 (1995), 1054--1078.

3. Shipra Agrawal and Navin Goyal . 2013. Thompson sampling for contextual bandits with linear payoffs International Conference on Machine Learning. 127--135.

4. Peter Auer, Nicolo Cesa-Bianchi, and Paul Fischer . 2002 a. Finite-time analysis of the multiarmed bandit problem. Machine learning, Vol. 47, 2--3 (2002), 235--256.

5. Peter Auer, Nicolo Cesa-Bianchi, Yoav Freund, and Robert E Schapire . 1995. Gambling in a rigged casino: The adversarial multi-armed bandit problem Foundations of Computer Science, 1995. Proceedings., 36th Annual Symposium on. IEEE, 322--331.

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