CTR学习笔记

1. 已完成模型列表

• FM
• FFM
• Embedding+MLP
• wide & Deep
• DeepFM
• PNN
• FNN

2. 参考论文列表

• [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
• [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
• [DIN] Deep Interest Network for Click-Through Rate Prediction.
• [DIEN] Deep Interest Evolution Network for Click-Through Rate Prediction
• [DCN] Deep & Cross Network for Ad Click Predictions
• [xDeepFM] xDeepFM- Combining Explicit and Implicit Feature Interactions for Recommender Systems

3. 总结博客

• CTR学习笔记&代码实现1-深度学习的前奏LR->FFM https://www.cnblogs.com/gogoSandy/p/12501846.html
• CTR学习笔记&代码实现2-深度ctr模型 MLP->Wide&Deep https://www.cnblogs.com/gogoSandy/p/12658051.html
• CTR学习笔记&代码实现3-深度ctr模型 FNN->PNN->DeepFM https://www.cnblogs.com/gogoSandy/p/12742417.html