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. <br /> 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

• [LR] Predicting Clicks - Estimating the Click-Through Rate for New Ads (Microsoft 2007) <br />
• [FFM] Field-aware Factorization Machines for CTR Prediction (Criteo 2016) <br />
• [GBDT+LR] Practical Lessons from Predicting Clicks on Ads at Facebook (Facebook 2014) <br />
• [PS-PLM] Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction (Alibaba 2017) <br />
• [FTRL] Ad Click Prediction a View from the Trenches (Google 2013) <br />
• [FM] Fast Context-aware Recommendations with Factorization Machines (UKON 2011) <br />
• [DCN] Deep & Cross Network for Ad Click Predictions (Stanford 2017) <br />
• [Deep Crossing] Deep Crossing - Web-Scale Modeling without Manually Crafted Combinatorial Features (Microsoft 2016) <br />
• [PNN] Product-based Neural Networks for User Response Prediction (SJTU 2016) <br />
• [DIN] Deep Interest Network for Click-Through Rate Prediction (Alibaba 2018) <br />
• [ESMM] Entire Space Multi-Task Model - An Effective Approach for Estimating Post-Click Conversion Rate (Alibaba 2018) <br />
• [Wide & Deep] Wide & Deep Learning for Recommender Systems (Google 2016) <br />
• [xDeepFM] xDeepFM - Combining Explicit and Implicit Feature Interactions for Recommender Systems (USTC 2018) <br />
• [Image CTR] Image Matters - Visually modeling user behaviors using Advanced Model Server (Alibaba 2018) <br />
• [AFM] Attentional Factorization Machines - Learning the Weight of Feature Interactions via Attention Networks (ZJU 2017) <br />
• [DIEN] Deep Interest Evolution Network for Click-Through Rate Prediction (Alibaba 2019) <br />
• [DSSM] Learning Deep Structured Semantic Models for Web Search using Clickthrough Data (UIUC 2013) <br />
• [FNN] Deep Learning over Multi-field Categorical Data (UCL 2016) <br />
• [DeepFM] A Factorization-Machine based Neural Network for CTR Prediction (HIT-Huawei 2017) <br />
• [NFM] Neural Factorization Machines for Sparse Predictive Analytics (NUS 2017) <br />

Other Resources

• Papers on Computational Advertising <br />
• Papers on Recommender System <br />