CTR Prediction and Ad Ranking System: A Complete Practice from Data to Deployment

This project presents an end-to-end CTR prediction workflow, covering data generation, feature engineering, multi-model training (logistic regression, gradient boosting, neural networks), offline evaluation, and ad ranking applications. Using tools like Python, TensorFlow, and scikit-learn, it provides reproducible practice cases for learners, bridging machine learning and business value.

CTR prediction is a core technology in digital advertising and recommendation systems. Its goal is to estimate the probability of user clicks, which affects ad ranking, bidding strategies, and delivery effectiveness. This project provides a complete end-to-end workflow (data generation → model training → offline evaluation) to help learners grasp practical key points.

Technology Stack: Python (main language), TensorFlow/Keras (neural networks), scikit-learn (logistic regression/gradient boosting), Pandas (data processing), NumPy (numerical computation).

Data Generation: Uses synthetic datasets, with advantages such as controllability, privacy, reproducibility, and scale flexibility. It simulates user profiles, context, and ad features.

Feature Engineering: Processes behavioral signals (user historical CTR, category distribution, etc.) and context signals (time, device, location, etc.) via encoding and normalization.

Model Comparison: 1. Logistic regression (baseline, simple, efficient, and interpretable); 2. Gradient boosting (captures non-linearity and feature interactions); 3. Neural networks (strong expressive power, supports end-to-end training). Each model has its own pros and cons.

Evaluation Metrics: AUC-ROC (distinguishes positive and negative samples), Log Loss (difference between predicted probability and true label), Precision/Recall (performance at specific thresholds).

Ranking Example: Calculates eCPM (CTR × bid ×1000) by combining predicted CTR with bids, then sorts ads in descending order of eCPM, which is the foundation of the GSP mechanism.

Quick Start: Install dependencies (pip install pandas numpy scikit-learn tensorflow), then run the training script (python train_ctr_model.py) to automatically generate data, train models, and output results.

Learning Value: Suitable for machine learning beginners, model comparison practice, feature engineering exercises, and understanding evaluation metrics.

Extension Directions: Introduce complex deep learning models (e.g., DeepFM), use real datasets (Criteo/Avazu), implement online learning, add model interpretability analysis, and deploy as a REST API.

Summary: This project demonstrates the complete workflow from data to model, serving as an ideal entry-level case that bridges machine learning and business value, and is worth referencing.