CTR prediction using online learning models for the Kaggle UCL Web Economics Algorithm Challenge 2016
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Updated
Apr 24, 2016 - Jupyter Notebook
CTR prediction using online learning models for the Kaggle UCL Web Economics Algorithm Challenge 2016
2nd place solution for Criteo Ad Placement challenge
Lord of the machines AV competition
2018 阿里妈妈搜索广告转化率预估 复赛最终成绩 rank 84/5204
Running field-aware factorization machines on the Criteo data
Tensorflow implementation of DeepFM for CTR prediction.
4th Place Solution for Mercari Price Suggestion Competition on Kaggle using DeepFM variant.
Click-Through Rate Prediction
用户兴趣建模大赛 top10 开源代码
Wide and Deep Learning(Wide&ResDNN) for Kaggle Criteo Dataset in tensorflow
Hybrid model of Gradient Boosting Trees and Logistic Regression (GBDT+LR) on Spark
implementation of "DeepFM: A Factorization-Machine based Neural Network for CTR Prediction"
ICME 2019 Grand Challenge——短视频内容理解与推荐竞赛
Tensorflow based field-aware factorization machine, FFM.
This is a TensorFlow implementation of Deep & Cross Network for CTR prediction task.
AlitaNet: A click through rate (ctr) prediction deep learning Network implementation with TensorFlow, including LR, FM, AFM, Wide&Deep, DeepFM, xDeepFM, AutoInt, FiBiNet, LS-PLM, DCN, etc.
Pure Python implementation of the Follow The Regularized Leader - Proximal algorithm
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