CTR学习笔记

The code is not rigorously tested, if you find a bug, welcome PR ^_^ ~

Run: python main.py --model DeepFM --step train --dataset census --clear_model 1
Requirement: tensorflow 1.15

[GBDT+LR] Practical Lessons from Predicting Clicks on Ads at Facebook
[FM] S. Rendle, Factorization machines
[FM Model] Fast Context-aware Recommendations with Factorization Machines
[FFM] Yuchin Juan，Yong Zhuang，Wei-Sheng Chin，Field-aware Factorization Machines for CTR Prediction
[NCF] Neural Collaborative Filtering
[Wide&Deep] Cheng H T, Koc L, Harmsen J, et al. Wide & deep learning for recommender systems
[FNN] Weinan Zhang, Tianming Du, and Jun Wang. Deep learning over multi-field categorical data - - A case study on user response
[PNN] Qu Y, Cai H, Ren K, et al. Product-based neural networks for user response prediction
[DeepFM] Huifeng Guo et all. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
[AFM] Attentional Factorization Machines - Learning the Weight of Feature Interactions via Attention Networks
[NFM] Neural Factorization Machines for Sparse Predictive Analytics
[DCN] Deep & Cross Network for Ad Click Predictions
[Deep Crossing] Deep Crossing - Web-Scale Modeling without Manually Crafted Combinatorial Features
[xDeepFM] xDeepFM- Combining Explicit and Implicit Feature Interactions for Recommender Systems
[FiBiNET]- Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
[AutoInt]- Automatic Feature Interaction Learning via Self-Attentive Neural Networks
[DIN] Deep Interest Network for Click-Through Rate Prediction.
[DIEN] Deep Interest Evolution Network for Click-Through Rate Prediction

DSXiangLi/CTR