【文件属性】：
文件名称：CTR:CTR模型代码和学习笔记总结
文件大小：30.21MB
文件格式：ZIP
更新时间：2021-03-21 04:28:38
afm tensorflow frappe recommendation-algorithms fm
CTR学习笔记
运行：python main.py --model DeepFM-步进火车-数据集普查--clear_model 1
要求：张量流1.15
已完成模型列表[支持数据集]
FM [普查]
实况调查团[人口普查]
嵌入+ MLP [普查]
广泛[深度]
FNN [普查]
PNN [普查]
DeepFM [普查和调查]
原子力显微镜[人口普查和frappe]
NFM [普查和调查]
深交[人口普查]
深入与交叉[普查与调查]
xDeepFM [人口普查和frappe]
FiBiNET [人口普查和frappe]
DIN [亚马逊]
数据集当前支持人口普查，frappe数据集，详情见数据目录，训练参数和预处理与数据集绑定
参考论文列表
[GBDT + LR]预测Facebook广告点击的实用经验
[FM] S. Rendle，分解机
[FM模型]使用分解机的快速上下
【文件预览】：
CTR-master
----.gitignore(183B)
----README.md(3KB)
----config.py(2KB)
----const()
--------frappe_const.py(93B)
--------amazon_const.py(731B)
--------census_const.py(2KB)
--------__init__.py(731B)
----model()
--------FNN()
--------PNN()
--------DCN()
--------AFM()
--------xDeepFM()
--------DeepCrossing()
--------FFM()
--------EMMLP()
--------FM()
--------FiBiNET()
--------DIEN()
--------DeepFM()
--------NFM()
--------wide_and_deep()
--------DIN()
----utils.py(6KB)
----main.py(3KB)
----__init__.py(0B)
----paper()
--------[xDeepFM]xDeepFM- Combining Explicit and Implicit Feature Interactions for Recommender Systems.pdf(1.44MB)
--------[AutoInt]- Automatic Feature Interaction Learning via Self-Attentive Neural Networks.pdf(1.52MB)
--------[PNN] Product-based Neural Networks for User Response Prediction (SJTU 2016).pdf(470KB)
--------[FFM]Field-aware Factorization Machines for CTR Prediction.pdf(361KB)
--------[DIN]Deep Interest Network for Click-Through Rate Prediction.pdf(8.13MB)
--------[AFM] Attentional Factorization Machines - Learning the Weight of Feature Interactions via Attention Networks (ZJU 2017).pdf(3.39MB)
--------[FM Method]Factorization Machines.pdf(187KB)
--------[AFM]Attentional Factorization Machines- Learning the Weight of Feature Interactions via Attention Networks∗.pdf(987KB)
--------[NCF] Neural Collaborative Filtering (NUS 2017).pdf(1.42MB)
--------[Wide&Deep] Wide & Deep Learning for Recommender Systems (Google 2016).pdf(480KB)
--------[Deep&Cross]Deep & Cross Network for Ad Click Predictions.pdf(232KB)
--------[Deep Crossing] Deep Crossing - Web-Scale Modeling without Manually Crafted Combinatorial Features (Microsoft 2016).pdf(434KB)
--------[NFM]Neural Factorization Machines for Sparse Predictive Analytics.pdf(3.49MB)
--------[DeepFM]DeepFM- A Factorization-Machine based Neural Network for CTR Prediction.pdf(1.14MB)
--------[FNN] Deep Learning over Multi-field Categorical Data (UCL 2016).pdf(566KB)
--------[GBDT+LR]Practical Lessons from Predicting Clicks on Ads at Facebook.pdf(774KB)
--------[DIEN]Deep Interest Evolution Network for Click-Through Rate Prediction.pdf(2.07MB)
--------[FiBiNET]- Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction.pdf(939KB)
--------[FM Model] Fast Context-aware Recommendations with Factorization Machines (UKON 2011).pdf(291KB)
----playground()
--------Embedding()
--------feature_columnn()
----layers.py(2KB)
----data()
--------amazon()
--------movie_len()
--------frappe()
--------ali_ccp()
--------census()