Sky-zzt/CTR

CTR模型代码和学习笔记总结

CTR学习笔记

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

1. 已完成模型列表[支持数据集]

• FM [census]
• FFM [census]
• Embedding+MLP [census]
• wide & Deep [census]
• FNN [census]
• PNN [census]
• DeepFM [census & frappe]
• AFM [census & frappe]
• NFM [census & frappe]
• Deep Crossing [census]
• Deep & Cross [census & frappe]
• xDeepFM [census & frappe]
• FiBiNET [census & frappe]
• DIN [amazon]

2. 数据集 当前支持census, frappe数据集，详情见data目录，training parameter和preprocess与数据集绑定

3. 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
• [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

4. 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
• CTR学习笔记&代码实现4-深度ctr模型 NFM/AFM https://www.cnblogs.com/gogoSandy/p/12814804.html
• CTR学习笔记&代码实现5-深度ctr模型 DeepCrossing -> DCN https://www.cnblogs.com/gogoSandy/p/12892973.html
• CTR学习笔记&代码实现6-深度ctr模型 后浪 xDeepFM/FiBiNET https://www.cnblogs.com/gogoSandy/p/13023265.html