# CTRmodel

CTR prediction model based on pure Spark MLlib, no third-party library.

# Realized Models

- Naive Bayes
- Logistic Regression
- Factorization Machine
- Random Forest
- Gradient Boosted Decision Tree
- GBDT + LR
- Neural Network
- Inner Product Neural Network (IPNN)
- Outer Product Neural Network (OPNN)

# Usage

It's a maven project. Spark version is 2.3.0. Scala version is 2.11.

After dependencies are imported by maven automatically, you can simple run the example function (**com.ggstar.example.ModelSelection**) to train all the CTR models and get the metrics comparison among all the models.

# Related Papers on CTR prediction

- [GBDT+LR]Practical Lessons from Predicting Clicks on Ads at Facebook.pdf
- [FNN]Deep Learning over Multi-field Categorical Data.pdf
- [Multi-Task]An Overview of Multi-Task Learning in Deep Neural Networks.pdf
- [PNN]Product-based Neural Networks for User Response Prediction.pdf
- [Wide & Deep]Wide & Deep Learning for Recommender Systems.pdf
- [DeepFM]- A Factorization-Machine based Neural Network for CTR Prediction.pdf
- Deep Crossing- Web-Scale Modeling without Manually Crafted Combinatorial Features.pdf
- Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction.pdf
- Entire Space Multi-Task Model_ An Effective Approach for Estimating Post-Click Conversion Rate.pdf
- Deep Interest Network for Click-Through Rate Prediction.pdf
- Bid-aware Gradient Descent for Unbiased Learning with Censored Data in Display Advertising.pdf
- Ad Click Prediction a View from the Trenches.pdf
- Image Matters- Visually modeling user behaviors using Advanced Model Server.pdf
- Logistic Regression in Rare Events Data.pdf
- Deep & Cross Network for Ad Click Predictions.pdf
- Learning Deep Structured Semantic Models for Web Search using Clickthrough Data.pdf
- Adaptive Targeting for Online Advertisement.pdf