Real-Time Detection of Robotic Traffic in Online Advertising
Anand Muralidhar, Sharad Chitlangia, Rajat Agarwal, Muneeb Ahmed
Abstract
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.
BibTeX
@article{Muralidhar_Chitlangia_Agarwal_Ahmed_2024, title={Real-Time Detection of Robotic Traffic in Online Advertising}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/26844}, DOI={10.1609/aaai.v37i13.26844}, abstractNote={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.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Muralidhar, Anand and Chitlangia, Sharad and Agarwal, Rajat and Ahmed, Muneeb}, year={2024}, month={Jul.}, pages={15551-15559} }