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)...