wzhe06/SparkCTR is ranked 12,619 among these 8 peers; HazyResearch/safari is ranked 12,620.

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. After dependencies are imported by maven automatically, you can simple run the example function (**com. 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)]( [[FFM] Field-aware Factorization Machines for CTR Prediction (Criteo 2016)]( [[GBDT+LR] Practical Lessons from Predicting Clicks on Ads at Facebook (Facebook 2014)]( [[PS-PLM] Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction (Alibaba 2017)]( [[FTRL] Ad Click Prediction a View from the Trenches (Google 2013)]( [[FM] Fast Context-aware Recommendations with Factorization Machines (UKON 2011)]( [[DCN] Deep & Cross Network

for Ad Click Predictions (Stanford 2017)]( [[Deep Crossing] Deep Crossing - Web-Scale Modeling without Manually Crafted Combinatorial Features (Microsoft 2016)]( [[PNN] Product-based Neural Networks for User Response Prediction (SJTU 2016)]( [[DIN] Deep Interest Network for Click-Through Rate Prediction (Alibaba 2018)]( [[ESMM] Entire Space Multi-Task Model - An Effective Approach for Estimating Post-Click Conversion Rate (Alibaba 2018)]( [[Wide & Deep] Wide & Deep Learning for Recommender Systems (Google 2016)]( [[xDeepFM] xDeepFM - Combining Explicit and Implicit Feature Interactions for Recommender Systems

(USTC 2018)]( [[Image CTR] Image Matters - Visually modeling user behaviors using Advanced Model Server (Alibaba 2018)]( [[AFM] Attentional Factorization Machines - Learning the Weight of Feature Interactions via Attention Networks (ZJU 2017)]( [[DIEN] Deep Interest Evolution Network for Click-Through Rate Prediction (Alibaba 2019)]( [[DSSM] Learning Deep Structured Semantic Models for Web Search using Clickthrough Data (UIUC 2013)]( [[FNN] Deep Learning over Multi-field Categorical Data (UCL 2016)]( [[DeepFM] A Factorization-Machine based Neural Network for CTR Prediction (HIT-Huawei 2017

)]( [[NFM] Neural Factorization Machines for Sparse Predictive Analytics (NUS 2017)]( Other Resources Papers on Computational Advertising Papers on Recommender System