"algo-ad-ctr"

CTR Prediction Model

Overview

CTR prediction estimates the probability that a user clicks on an ad given context (user, query, ad, position). Forms the core of ad ranking: AdRank = Bid × pCTR. Typically uses logistic regression or gradient-boosted trees. Training on billions of impressions.

When to Use

Trigger conditions:

• Building or improving an ad ranking system
• Predicting click probability for bid optimization
• Evaluating ad creative effectiveness from feature analysis

When NOT to use:

• When predicting post-click conversions (use conversion rate model)
• When setting bid amounts (use bidding strategy skill)

Algorithm

IRON LAW: A CTR Model Must Be CALIBRATED
Predicting relative ranking is insufficient. The predicted probability
must MATCH actual click frequency (e.g., predicted 5% → 5 clicks per
100 impressions). Without calibration, bid optimization breaks:
  Expected Value = Bid × pCTR × pConversion
  If pCTR is off by 2x, bids are wrong by 2x.

Phase 1: Input Validation

Collect impression logs with: user features, ad features, query features, position, click label (0/1). Handle class imbalance (CTR typically 1-5%). Gate: Sufficient volume (100K+ impressions), click labels verified, no data leakage from position.

Phase 2: Core Algorithm

1. Feature engineering: user demographics, ad category, query-ad match, historical CTR, time/device features
2. Train model: logistic regression (interpretable) or GBDT (higher accuracy)
3. Calibrate predictions: Platt scaling or isotonic regression on holdout set
4. Evaluate: log-loss (calibration) + AUC (ranking quality)

Phase 3: Verification

Check calibration: bucket predictions into deciles, compare predicted vs actual CTR per bucket. Plot reliability diagram. Gate: Calibration curve close to diagonal, AUC > 0.70.

Phase 4: Output

Return predicted CTR with confidence interval and top contributing features.

Output Format

{
  "prediction": {"ctr": 0.035, "confidence_interval": [0.028, 0.042]},
  "top_features": [{"feature": "query_ad_match", "importance": 0.32}],
  "metadata": {"model": "gbdt", "auc": 0.78, "log_loss": 0.21, "calibration_error": 0.008}
}