# 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. Spark version is 2.3.0. Scala version is 2.11. <br />
After dependencies are imported by maven automatically, you can simple run the example function (**com.ggstar.example.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)](https://github.com/wzhe06/Ad-papers/blob/master/Classic%20CTR%20Prediction/%5BLR%5D%20Predicting%20Clicks%20-%20Estimating%20the%20Click-Through%20Rate%20for%20New%20Ads%20%28Microsoft%202007%29.pdf) <br />
* [[FFM] Field-aware Factorization Machines for CTR Prediction (Criteo 2016)](https://github.com/wzhe06/Ad-papers/blob/master/Classic%20CTR%20Prediction/%5BFFM%5D%20Field-aware%20Factorization%20Machines%20for%20CTR%20Prediction%20%28Criteo%202016%29.pdf) <br />
* [[GBDT+LR] Practical Lessons from Predicting Clicks on Ads at Facebook (Facebook 2014)](https://github.com/wzhe06/Ad-papers/blob/master/Classic%20CTR%20Prediction/%5BGBDT%2BLR%5D%20Practical%20Lessons%20from%20Predicting%20Clicks%20on%20Ads%20at%20Facebook%20%28Facebook%202014%29.pdf) <br />
* [[PS-PLM] Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction (Alibaba 2017)](https://github.com/wzhe06/Ad-papers/blob/master/Classic%20CTR%20Prediction/%5BPS-PLM%5D%20Learning%20Piece-wise%20Linear%20Models%20from%20Large%20Scale%20Data%20for%20Ad%20Click%20Prediction%20%28Alibaba%202017%29.pdf) <br />
* [[FTRL] Ad Click Prediction a View from the Trenches (Google 2013)](https://github.com/wzhe06/Ad-papers/blob/master/Classic%20CTR%20Prediction/%5BFTRL%5D%20Ad%20Click%20Prediction%20a%20View%20from%20the%20Trenches%20%28Google%202013%29.pdf) <br />
* [[FM] Fast Context-aware Recommendations with Factorization Machines (UKON 2011)](https://github.com/wzhe06/Ad-papers/blob/master/Classic%20CTR%20Prediction/%5BFM%5D%20Fast%20Context-aware%20Recommendations%20with%20Factorization%20Machines%20%28UKON%202011%29.pdf) <br />
* [[DCN] Deep & Cross Network for Ad Click Predictions (Stanford 2017)](https://github.com/wzhe06/Ad-papers/blob/master/Deep%20Learning%20CTR%20Prediction/%5BDCN%5D%20Deep%20%26%20Cross%20Network%20for%20Ad%20Click%20Predictions%20%28Stanford%202017%29.pdf) <br />
* [[Deep Crossing] Deep Crossing - Web-Scale Modeling without Manually Crafted Combinatorial Features (Microsoft 2016)](https://github.com/wzhe06/Ad-papers/blob/master/Deep%20Learning%20CTR%20Prediction/%5BDeep%20Crossing%5D%20Deep%20Crossing%20-%20Web-Scale%20Modeling%20without%20Manually%20Crafted%20Combinatorial%20Features%20%28Microsoft%202016%29.pdf) <br />
* [[PNN] Product-based Neural Networks for User Response Prediction (SJTU 2016)](https://github.com/wzhe06/Ad-papers/blob/master/Deep%20Learning%20CTR%20Prediction/%5BPNN%5D%20Product-based%20Neural%20Networks%20for%20User%20Response%20Prediction%20%28SJTU%202016%29.pdf) <br />
* [[DIN] Deep Interest Network for Click-Through Rate Prediction (Alibaba 2018)](https://github.com/wzhe06/Ad-papers/blob/master/Deep%20Learning%20CTR%20Prediction/%5BDIN%5D%20Deep%20Interest%20Network%20for%20Click-Through%20Rate%20Prediction%20%28Alibaba%202018%29.pdf) <br />
* [[ESMM] Entire Space Multi-Task Model - An Effective Approach for Estimating Post-Click Conversion Rate (Alibaba 2018)](https://github.com/wzhe06/Ad-papers/blob/master/Deep%20Learning%20CTR%20Prediction/%5BESMM%5D%20Entire%20Space%20Multi-Task%20Model%20-%20An%20Effective%20Approach%20for%20Estimating%20Post-Click%20Conversion%20Rate%20%28Alibaba%202018%29.pdf) <br />
* [[Wide & Deep] Wide & Deep Learning for Recommender Systems (Google 2016)](https://github.com/wzhe06/Ad-papers/blob/master/Deep%20Learning%20CTR%20Prediction/%5BWide%20%26%20Deep%5D%20Wide%20%26%20Deep%20Learning%20for%20Recommender%20Systems%20%28Google%202016%29.pdf) <br />
* [[xDeepFM] xDeepFM - Combining Explicit and Implicit Feature Interactions for Recommender Systems (USTC 2018)](https://github.com/wzhe06/Ad-papers/blob/master/Deep%20Learning%20CTR%20Prediction/%5BxDeepFM%5D%20xDeepFM%20-%20Combining%20Explicit%20and%20Implicit%20Feature%20Interactions%20for%20Recommender%20Systems%20%28USTC%202018%29.pdf) <br />
* [[Image CTR] Image Matters - Visually modeling user behaviors using Advanced Model Server (Alibaba 2018)](https://github.com/wzhe06/Ad-papers/blob/master/Deep%20Learning%20CTR%20Prediction/%5BImage%20CTR%5D%20Image%20Matters%20-%20Visually%20modeling%20user%20behaviors%20using%20Advanced%20Model%20Server%20%28Alibaba%202018%29.pdf) <br />
* [[AFM] Attentional Factorization Machines - Learning the Weight of Feature Interactions via Attention Networks (ZJU 2017)](https://github.com/wzhe06/Ad-papers/blob/master/Deep%20Learning%20CTR%20Prediction/%5BAFM%5D%20Attentional%20Factorization%20Machines%20-%20Learning%20the%20Weight%20of%20Feature%20Interactions%20via%20Attention%20Networks%20%28ZJU%202017%29.pdf) <br />
* [[DIEN] Deep Interest Evolution Network for Click-Through Rate Prediction (Alibaba 2019)](https://github.com/wzhe06/Ad-papers/blob/master/Deep%20Learning%20CTR%20Prediction/%5BDIEN%5D%20Deep%20Interest%20Evolution%20Network%20for%20Click-Through%20Rate%20Prediction%20%28Alibaba%202019%29.pdf) <br />
* [[DSSM] Learning Deep Structured Semantic Models for Web Search using Clickthrough Data (UIUC 2013)](https://github.com/wzhe06/Ad-papers/blob/master/Deep%20Learning%20CTR%20Prediction/%5BDSSM%5D%20Learning%20Deep%20Structured%20Semantic%20Models%20for%20Web%20Search%20using%20Clickthrough%20Data%20%28UIUC%202013%29.pdf) <br />
* [[FNN] Deep Learning over Multi-field Categorical Data (UCL 2016)](https://github.com/wzhe06/Ad-papers/blob/master/Deep%20Learning%20CTR%20Prediction/%5BFNN%5D%20Deep%20Learning%20over%20Multi-field%20Categorical%20Data%20%28UCL%202016%29.pdf) <br />
* [[DeepFM] A Factorization-Machine based Neural Network for CTR Prediction (HIT-Huawei 2017)](https://github.com/wzhe06/Ad-papers/blob/master/Deep%20Learning%20CTR%20Prediction/%5BDeepFM%5D%20A%20Factorization-Machine%20based%20Neural%20Network%20for%20CTR%20Prediction%20%28HIT-Huawei%202017%29.pdf) <br />
* [[NFM] Neural Factorization Machines for Sparse Predictive Analytics (NUS 2017)](https://github.com/wzhe06/Ad-papers/blob/master/Deep%20Learning%20CTR%20Prediction/%5BNFM%5D%20Neural%20Factorization%20Machines%20for%20Sparse%20Predictive%20Analytics%20%28NUS%202017%29.pdf) <br />
# Other Resources
* [Papers on Computational Advertising](https://github.com/wzhe06/Ad-papers) <br />
* [Papers on Recommender System](https://github.com/wzhe06/Ad-papers) <br />
没有合适的资源?快使用搜索试试~ 我知道了~
SparkCTR:基于spark(LR,GBDT,DNN)的CTR预测模型
共36个文件
scala:23个
java:6个
xml:2个
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点击率模型 基于纯Spark MLlib的CTR预测模型,无第三方库。 实现模型 朴素贝叶斯 逻辑回归 分解机 随机森林 梯度提升决策树 GBDT + LR 神经网络 内部产品神经网络(IPNN) 外部产品神经网络(OPNN) 用法 这是一个行家项目。 Spark版本是2.3.0。 Scala版本是2.11。 在maven自动导入依赖项之后,您可以简单地运行示例函数( com.ggstar.example.ModelSelection )来训练所有CTR模型并获得所有模型之间的指标比较。 有关点击率预测的相关论文 其他资源
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收起资源包目录
SparkCTR-master.zip (36个子文件)
SparkCTR-master
.gitignore 22B
model
inn.model.mleap.zip 9KB
inn.model.jpmml.xml 29KB
src
main
com
ggstar
evaluation
Evaluator.scala 662B
serving
mleap
serialization
ModelSerializer.scala 725B
load
ModelServer.scala 915B
JavaModelServer.java 1KB
jpmml
serialization
ModelSerializer.scala 653B
load
JavaModelServer.java 2KB
util
Scala2JavaConverter.scala 177B
ctrmodel
InnerProductNNCtrModel.scala 2KB
RandomForestCtrModel.scala 816B
NeuralNetworkCtrModel.scala 1KB
NaiveBayesCtrModel.scala 731B
FactorizationMachineCtrModel.scala 1KB
BaseCtrModel.scala 302B
GBTLRCtrModel.scala 1KB
GBDTCtrModel.scala 740B
OuterProductNNCtrModel.scala 2KB
LogisticRegressionCtrModel.scala 888B
example
MLeapModelServing.scala 1KB
ModelServingPerformance.java 9KB
ModelSerialization.scala 2KB
JPMMLModelServing.java 1KB
Sample.java 3KB
MLeapJavaModelServing.java 2KB
ModelSelection.scala 3KB
features
FeatureEngineering.scala 8KB
org
apache
spark
mllib
regression
FMWithLBFGS.scala 8KB
FactorizationMachine.scala 10KB
FMWithSGD.scala 9KB
ml
gbtlr
GBTLRClassifier.scala 37KB
LICENSE 11KB
_config.yml 26B
pom.xml 3KB
README.md 7KB
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