René's URL Explorer Experiment

Title: GitHub - recomendation/RecSys: 计算广告/推荐系统/机器学习(Machine Learning)/点击率(CTR)/转化率(CVR)预估/点击率预估 · GitHub

Open Graph Title: GitHub - recomendation/RecSys: 计算广告/推荐系统/机器学习(Machine Learning)/点击率(CTR)/转化率(CVR)预估/点击率预估

X Title: GitHub - recomendation/RecSys: 计算广告/推荐系统/机器学习(Machine Learning)/点击率(CTR)/转化率(CVR)预估/点击率预估

Description: 计算广告/推荐系统/机器学习(Machine Learning)/点击率(CTR)/转化率(CVR)预估/点击率预估 - recomendation/RecSys

Open Graph Description: 计算广告/推荐系统/机器学习(Machine Learning)/点击率(CTR)/转化率(CVR)预估/点击率预估 - recomendation/RecSys

X Description: 计算广告/推荐系统/机器学习(Machine Learning)/点击率(CTR)/转化率(CVR)预估/点击率预估 - recomendation/RecSys

Opengraph URL: https://github.com/recomendation/RecSys

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RecSys	https://github.com/recomendation/RecSys
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	https://github.com/recomendation/RecSys/commits/master/
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README	https://github.com/recomendation/RecSys
	https://github.com/recomendation/RecSys#推荐系统计算广告机器学习ctr预估资料汇总
	https://github.com/recomendation/RecSys/blob/master/LICENSE.md
	https://camo.githubusercontent.com/e35b7d44ad2a822b4391d5c4b1b51a1432d3c142005f4a64c0e81563b4c07ad8/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7570646174652d7765656b6c792d677265656e2e737667
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	https://github.com/recomendation/RecSys#roadmap
推荐系统	https://github.com/recomendation/RecSys#%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F
计算广告	https://github.com/recomendation/RecSys#%E8%AE%A1%E7%AE%97%E5%B9%BF%E5%91%8A
统计学习模型	https://github.com/recomendation/RecSys#%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%A8%A1%E5%9E%8B
技术文章	https://github.com/recomendation/RecSys#%E6%8A%80%E6%9C%AF%E6%96%87%E7%AB%A0
实践工具	https://github.com/recomendation/RecSys#%E5%AE%9E%E8%B7%B5%E5%B7%A5%E5%85%B7
深度学习模型	https://github.com/recomendation/RecSys#%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E6%A8%A1%E5%9E%8B
技术文章	https://github.com/recomendation/RecSys#%E6%8A%80%E6%9C%AF%E6%96%87%E7%AB%A0
实践代码	https://github.com/recomendation/RecSys#%E5%AE%9E%E8%B7%B5%E4%BB%A3%E7%A0%81
相关比赛	https://github.com/recomendation/RecSys#%E7%9B%B8%E5%85%B3%E6%AF%94%E8%B5%9B
Criteo Display Advertising Challenge	https://github.com/recomendation/RecSys#CriteoDisplayAdvertisingChallenge
Avazu Click Through Rate Prediction	https://github.com/recomendation/RecSys#AvazuClickThroughRatePrediction
2018 IJCAI 阿里妈妈搜索广告转化预测	https://github.com/recomendation/RecSys#2018-IJCAI-%E9%98%BF%E9%87%8C%E5%A6%88%E5%A6%88%E6%90%9C%E7%B4%A2%E5%B9%BF%E5%91%8A%E8%BD%AC%E5%8C%96%E9%A2%84%E6%B5%8B
2018腾讯广告算法大赛	https://github.com/recomendation/RecSys#2018%E8%85%BE%E8%AE%AF%E5%B9%BF%E5%91%8A%E7%AE%97%E6%B3%95%E5%A4%A7%E8%B5%9B
框架工具	https://github.com/recomendation/RecSys#%E6%A1%86%E6%9E%B6%E5%B7%A5%E5%85%B7
	https://github.com/recomendation/RecSys#推荐系统
推荐系统理论及实战系列博文-石晓文	https://www.jianshu.com/nb/21403842
推荐系统实践-项亮	https://github.com/recomendation/RecSys/blob/master/resource/%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F%E5%AE%9E%E8%B7%B5-%E9%A1%B9%E4%BA%AE.pdf
亿级用户个性化品类推荐实战	https://gitbook.cn/books/5acc23b4f453ee79e417c729/index.html
	https://github.com/recomendation/RecSys#计算广告
互联网广告系统综述系列博文	https://blog.csdn.net/mytestmy/article/list
计算广告学系列视屏-刘鹏	https://study.163.com/course/introduction.htm?courseId=321007#/courseDetail?tab=1
计算广告学讲义-刘鹏	https://dirtysalt.github.io/html/computational-advertising.html
	https://github.com/recomendation/RecSys#统计学习模型
	https://github.com/recomendation/RecSys#技术文章
逻辑回归LR模型简介	https://tech.meituan.com/2015/05/08/intro-to-logistic-regression.html
FFM讲解PPT	https://github.com/recomendation/RecSys/blob/master/resource/ffm.pdf
深入FFM原理与实践	https://tech.meituan.com/2016/03/03/deep-understanding-of-ffm-principles-and-practices.html
GBDT算法原理与系统设计简介	https://github.com/recomendation/RecSys/blob/master/resource/GBDT-wepon.pdf
	https://github.com/recomendation/RecSys#实践工具
LightGBM	https://github.com/Microsoft/LightGBM
XGBoost	https://github.com/dmlc/xgboost
LIBFFM	https://github.com/guestwalk/libffm
xLearn	https://github.com/aksnzhy/xlearn
	https://github.com/recomendation/RecSys#深度学习模型
	https://github.com/recomendation/RecSys#技术文章-1
深度学习如何应用在广告、推荐及搜索业务？	https://mp.weixin.qq.com/s/nboZ6p_l30L__FJNyz6Ohw
深度学习在CTR预估中的应用	https://zhuanlan.zhihu.com/p/35484389
深度学习在 CTR 中应用	http://www.mamicode.com/info-detail-1990002.html
深度学习在美团点评推荐业务中实践	https://gitbook.cn/books/5aa0dd15cfbe2c144b71906d/index.html
	https://github.com/recomendation/RecSys#实践代码
CTR预估算法之FM, FFM, DeepFM及实践	https://github.com/Johnson0722/CTR_Prediction
推荐系统中使用ctr排序的dnn模型	https://github.com/nzc/dnn_ctr
	https://github.com/recomendation/RecSys#相关比赛
CriteoDisplayAdvertisingChallenge	https://www.kaggle.com/c/criteo-display-ad-challenge
	https://github.com/recomendation/RecSys#criteodisplayadvertisingchallenge
借鉴了Facebook的方案: GBDT 特征编码 + FFM	https://www.kaggle.com/c/criteo-display-ad-challenge/discussion/10555
Quadratic Feature Generation + FTRL 传统特征工程和 FTRL 线性模型的结合	https://www.kaggle.com/c/criteo-display-ad-challenge/discussion/10534
AvazuClickThroughRatePrediction	https://www.kaggle.com/c/avazu-ctr-prediction
	https://github.com/recomendation/RecSys#avazuclickthroughrateprediction
Feature Engineering + FFM + Ensemble, 只基于 FFM 进行集成	https://www.kaggle.com/c/avazu-ctr-prediction/discussion/12608
Feature Engineering + GBDT 特征编码 + FFM + Blending	https://github.com/owenzhang/kaggle-avazu
2018 IJCAI 阿里妈妈搜索广告转化预测	https://tianchi.aliyun.com/competition/entrance/231647/introduction?spm=5176.12281957.1004.10.38b04c2aaROEf9
	https://github.com/recomendation/RecSys#2018-ijcai-阿里妈妈搜索广告转化预测
GBDT，用了嫁接技术处理样本分布不一致	https://github.com/plantsgo/ijcai-2018
GBDT单模型+大量特征工程	https://github.com/YouChouNoBB/ijcai-18-top2-single-mole-solution
2018腾讯广告算法大赛	https://algo.qq.com/
	https://github.com/recomendation/RecSys#2018腾讯广告算法大赛
https://github.com/DiligentPanda/Tencent_Ads_Algo_2018	https://github.com/DiligentPanda/Tencent_Ads_Algo_2018
https://github.com/nzc/tencent-contest	https://github.com/nzc/tencent-contest
	https://github.com/recomendation/RecSys#todo
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