Optimal online learning in bidding for sponsored search auctions

Donghun Lee, Piotr Zioło, Weidong Han, Warren Buckler Powell

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Sponsored search advertisement auctions offer one of the most accessible automated bidding platforms to online advertisers via human-friendly web interfaces. We formulate the learning of the optimal bidding policy for sponsored search auctions as a stochastic optimization problem, and model the auctions to build a simulator based on a real world dataset focusing on the simulated sponsored search auctions to show hourly variations in auction frequency, click propensity, bidding competition, and revenue originating from advertisements. We present several bidding policies that learn from bidding results and that can be easily implemented. We also present a knowledge gradient learning policy that can guide bidding to generate samples from which the bidding policies learn. The bidding policies can be trained with a small number of samples to achieve a significant performance in advertising profit. We show that a hybrid policy that makes the optimal switching from learning the bidding policies to exploiting the learned bidding policies achieves 95% of optimal performance. Also, our result suggests that using knowledge gradient learning policy may provide robustness in guessing when to switch from learning the bidding policies to exploiting the learned bidding policies.

Original languageEnglish (US)
Title of host publication2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-8
Number of pages8
ISBN (Electronic)9781538627259
DOIs
StatePublished - Feb 2 2018
Event2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Honolulu, United States
Duration: Nov 27 2017Dec 1 2017

Publication series

Name2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
Volume2018-January

Other

Other2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
CountryUnited States
CityHonolulu
Period11/27/1712/1/17

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Optimization

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  • Cite this

    Lee, D., Zioło, P., Han, W., & Powell, W. B. (2018). Optimal online learning in bidding for sponsored search auctions. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings (pp. 1-8). (2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2017.8285393