• Corpus ID: 246430371

Challenges and approaches to privacy preserving post-click conversion prediction

@article{OBrien2022ChallengesAA,
  title={Challenges and approaches to privacy preserving post-click conversion prediction},
  author={Conor O'Brien and Arvind Thiagarajan and Sourav Das and Rafael Barreto and Chetan Kumar Verma and Tim Hsu and James Neufeld and Jonathan J. Hunt},
  journal={ArXiv},
  year={2022},
  volume={abs/2201.12666}
}
Online advertising has typically been more personalized than offline advertising, through the use of machine learning models and real-time auctions for ad targeting. One specific task, predicting the likelihood of conversion (i.e. the probability a user will purchase the advertised product), is crucial to the advertising ecosystem for both targeting and pricing ads. Currently, these models are often trained by observing individual user behavior, but, increasingly, regulatory and technical… 

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