Problem Framing and Feature Engineering
~40 min read
What CTR prediction is, why it's necessary for ad auctions, and the four feature categories that power a CTR model — with emphasis on cross-features as the highest-signal input type.
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Key points
- •CTR prediction is binary classification (click/no-click) at 1B+ impressions/day with the additional requirement of calibrated probabilities (for auction pricing) beyond just ranking quality
- •Cross-features (interactions between user, ad, and context features) consistently carry more predictive signal than any individual feature category — especially sparse cross-features like user_app_history × ad_category
- •Class imbalance (~2% click rate) requires negative downsampling in training, which introduces calibration bias that must be explicitly corrected post-training