文件名称：IJCAI-18-Alimama-Sponsored-Search-Conversion-Rate-CVR-Prediction-Contest
文件大小：5.68MB
文件格式：ZIP
更新时间：2024-06-07 23:28:00
JupyterNotebook
IJCAI-18-Alimama-Sponsored-Search-Conversion-Rate-CVR-Prediction-Contest 赛题详情 https://tianchi.aliyun.com/competition/introduction.htm?spm=5176.100150.711.5.18af2009XtMy26&raceId=231647 题目描述： 本次比赛以阿里电商广告为研究对象，提供了淘宝平台的海量真实交易数据，参赛选手通过人工智能技术构建预测模型预估用户的购买意向。 方案说明 ./Feature: feature.py 人工特征提取：具体说明见"特征检查.ipynb" one_hot_feature.py 利用xgboost提取组合特征，并加入类别one-hot组合特征 ./Model: Xgboost.ipynb xgboost模型训练，输出结果，并保
【文件预览】：
IJCAI-18-Alimama-Sponsored-Search-Conversion-Rate-CVR-Prediction-Contest-master
----.gitignore(12B)
----.ipynb_checkpoints()
--------结果对比-checkpoint.ipynb(29KB)
----Tool()
--------utils.py(417B)
--------config.py(3KB)
--------__pycache__()
----Model()
--------.ipynb_checkpoints()
--------DeepFM.py(18KB)
--------Xgboost.ipynb(411KB)
--------Blending.py(1KB)
--------Xgboost + LR.ipynb(14KB)
--------Stacking.py(5KB)
--------__pycache__()
--------yellowfin.py(19KB)
--------smooth.py(4KB)
--------xgboost + FM_FTRL.ipynb(10KB)
--------Lightgbm.ipynb(428KB)
----Feature()
--------.ipynb_checkpoints()
--------特征检查.ipynb(15KB)
--------feature.py(60KB)
--------one_hot_feature.py(8KB)
--------__pycache__()
----README.md(2KB)
----结果对比.ipynb(29KB)
----.idea()
--------misc.xml(265B)
--------IJCAI2018.iml(398B)
--------workspace.xml(43KB)
--------vcs.xml(180B)
--------modules.xml(270B)
----Picture()
--------lightgbm_feature.png(275KB)
--------xgboost_feature.png(280KB)
----Paper()
--------Practical Lessons from Predicting Clicks on Ads at Facebook.pdf(1.21MB)
--------Factorization Machines with Follow-The-Regularized-Leader for CTR prediction in.pdf(91KB)
--------DeepFM A Factorization—Machine based Neural Network for CTR Prediction.pdf(1.14MB)
--------Field-aware Factorization Machines in a Real-world Online.pdf(475KB)
--------Ad Click Prediction a View from the Trenches.pdf(1.45MB)
----Cache()
--------缓存文件.md(0B)