Real-Time Detection of Robotic Traffic in Online Advertising
DOI:
https://doi.org/10.1609/aaai.v37i13.26844Keywords:
Invalid Traffic Detection, Click Bot Detection, Digital Advertising, Slice-level Calibration, Weak Labeling, Deep Neural NetworksAbstract
Detecting robotic traffic at scale on online ads needs an approach that is scalable, comprehensive, precise, and can rapidly respond to changing traffic patterns. In this paper we describe SLIDR or SLIce-Level Detection of Robots, a real-time deep neural network model trained with weak supervision to identify invalid clicks on online ads. We ensure fairness across different traffic slices by formulating a convex optimization problem that allows SLIDR to achieve optimal performance on individual traffic slices with a budget on overall false positives. SLIDR has been deployed since 2021 and safeguards advertiser campaigns on Amazon against robots clicking on ads on the e-commerce site. We describe some of the important lessons learned by deploying SLIDR that include guardrails that prevent updates of anomalous models and disaster recovery mechanisms to mitigate or correct decisions made by a faulty model.Downloads
Published
2023-09-06
How to Cite
Muralidhar, A., Chitlangia, S., Agarwal, R., & Ahmed, M. (2023). Real-Time Detection of Robotic Traffic in Online Advertising. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 15551-15559. https://doi.org/10.1609/aaai.v37i13.26844
Issue
Section
IAAI Technical Track on deployed Highly Innovative Applications of AI