Online Evaluation of Audiences for Targeted Advertising via Bandit Experiments

Authors

  • Tong Geng JD.com
  • Xiliang Lin JD.com
  • Harikesh S. Nair JD.com and Stanford University

DOI:

https://doi.org/10.1609/aaai.v34i08.7036

Abstract

Firms implementing digital advertising campaigns face a complex problem in determining the right match between their advertising creatives and target audiences. Typical solutions to the problem have leveraged non-experimental methods, or used “split-testing” strategies that have not explicitly addressed the complexities induced by targeted audiences that can potentially overlap with one another. This paper presents an adaptive algorithm that addresses the problem via online experimentation. The algorithm is set up as a contextual bandit and addresses the overlap issue by partitioning the target audiences into disjoint, non-overlapping sub-populations. It learns an optimal creative display policy in the disjoint space, while assessing in parallel which creative has the best match in the space of possibly overlapping target audiences. Experiments show that the proposed method is more efficient compared to naive “split-testing” or non-adaptive “A/B/n” testing based methods. We also describe a testing product we built that uses the algorithm. The product is currently deployed on the advertising platform of JD.com, an eCommerce company and a publisher of digital ads in China.

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Published

2020-04-03

How to Cite

Geng, T., Lin, X., & Nair, H. S. (2020). Online Evaluation of Audiences for Targeted Advertising via Bandit Experiments. Proceedings of the AAAI Conference on Artificial Intelligence, 34(08), 13273-13279. https://doi.org/10.1609/aaai.v34i08.7036

Issue

Section

IAAI Technical Track: Emerging Papers