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Deep Learning for Click Prediction in Mobile AdTech
The article examines the transition from logistic regression to deep learning in mobile advertising's real-time bidding systems, focusing on click prediction models. It provides practical implementati
ons of both approaches using a Kaggle dataset, demonstrating how deep learning can overcome limitations of traditional methods while introducing new opportunities for improvement.

Reasons to Read -- Learn:

  • how real-time bidding systems work in practice, including specific formulas for calculating bid prices and handling over 5 million requests per second within 120-millisecond latency requirements
  • practical differences between logistic regression and deep learning approaches in click prediction, with detailed code examples and performance comparisons showing concrete improvements in metrics like PR AUC (0.09054 to 0.09249)
  • how embedding layers in deep neural networks can efficiently handle high-cardinality features in advertising data, reducing model complexity while maintaining or improving performance
  • 12 min readauthor: Ben Weber
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