From 0652a212ce10bf545725b6256bb643a306dcc0aa Mon Sep 17 00:00:00 2001 From: zhwang Date: Mon, 4 Mar 2019 13:34:05 -0800 Subject: [PATCH] upload DIEN paper --- ...work for Click-Through Rate Prediction.pdf | Bin 0 -> 2167707 bytes ...ork for Click-Through Rate Prediction.pdf} | Bin ...rver for Distributed Machine Learning.pdf} | Bin ...ne Learning with the Parameter Server.pdf} | Bin README.md | 18 +++++++++--------- 5 files changed, 9 insertions(+), 9 deletions(-) create mode 100644 CTR Prediction/[DIEN]Deep Interest Evolution Network for Click-Through Rate Prediction.pdf rename CTR Prediction/{Deep Interest Network for Click-Through Rate Prediction.pdf => [DIN]Deep Interest Network for Click-Through Rate Prediction.pdf} (100%) rename Computational Advertising Architect/{Parameter Server for Distributed Machine Learning.pdf => [Parameter Server]Parameter Server for Distributed Machine Learning.pdf} (100%) rename Computational Advertising Architect/{Scaling Distributed Machine Learning 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zu*26B7SePjTG}`IfoPVORhnI)Rg{G}Rb@Nq1q!Ti4|}O#1_gC}i<%DD8R$coRX+>aiqzW`zUpn#kJ)W{9I2 z57aMte}S3nVk=y+9Mxq!F?QMee8uQoD%;x#|!6Gu%R4grPcKjE2mmh#)Midae#)g z&_7dO#~uQ$Sq%%Uz3dwjQl%dnv!GWn6aS!`g9N^-N0bwUg_+SxbJ}9lyClb%Ukkvpv+-WzDY5+WCd97#fg$%?3vJ@z$SCtQy+p|P6t?tF{i6>8z~A^O8015 zFd!>==VU~<;4Is{I1NZ0qsB z|EIzg!M{4vN|}buuiV6PSO~se(hF99<7ywpoe3#_#x{}&Pp3T@J#6cu$v>Lz@9x3b zr;}Y2yzGbeo{n~RclVw$H%80Z=-D6l{%E#`@_cW9e;>nC#M9|67a1(J_Ma{HiTRC; z{l~IL*?#%#7!w5Mr%bSSj*rjyMep?M;lX!Am0}-hNBo#t+etVC1jiose)OXc@89YD Ef9|{vBLDyZ literal 0 HcmV?d00001 diff --git a/CTR Prediction/Deep Interest Network for Click-Through Rate Prediction.pdf b/CTR Prediction/[DIN]Deep Interest Network for Click-Through Rate Prediction.pdf similarity index 100% rename from CTR Prediction/Deep Interest Network for Click-Through Rate Prediction.pdf rename to CTR Prediction/[DIN]Deep Interest Network for Click-Through Rate Prediction.pdf diff --git a/Computational Advertising Architect/Parameter Server for Distributed Machine Learning.pdf b/Computational Advertising Architect/[Parameter Server]Parameter Server for Distributed Machine Learning.pdf similarity index 100% rename from Computational Advertising Architect/Parameter Server for Distributed Machine Learning.pdf rename to Computational Advertising Architect/[Parameter Server]Parameter Server for Distributed Machine Learning.pdf diff --git a/Computational Advertising Architect/Scaling Distributed Machine Learning with the Parameter Server.pdf b/Computational Advertising Architect/[Parameter Server]Scaling Distributed Machine Learning with the Parameter Server.pdf similarity index 100% rename from Computational Advertising Architect/Scaling Distributed Machine Learning with the Parameter Server.pdf rename to Computational Advertising Architect/[Parameter Server]Scaling Distributed Machine Learning with the Parameter Server.pdf diff --git a/README.md b/README.md index b587acf..06ff72e 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # 计算广告论文、学习资料、业界分享 动态更新工作中实现或者阅读过的计算广告相关论文、学习资料和业界分享,作为自己工作的总结,也希望能为计算广告相关行业的同学带来便利。 -所有资料均来自于互联网,如有侵权,请联系_王喆_。同时欢迎对计算广告感兴趣的同学与我讨论相关问题,我的联系方式如下: +所有资料均来自于互联网,如有侵权,请联系王喆。同时欢迎对计算广告感兴趣的同学与我讨论相关问题,我的联系方式如下: * Email: wzhe06@gmail.com * LinkedIn: [王喆的LinkedIn](https://www.linkedin.com/in/zhe-wang-profile/) * 知乎私信: [王喆的知乎](https://www.zhihu.com/people/wang-zhe-58) @@ -8,8 +8,8 @@ **会不断加入一些重要的计算广告相关论文和资料,并去掉一些过时的或者跟计算广告不太相关的论文** * `New!` [Image Matters- Visually modeling user behaviors using Advanced Model Server.pdf](https://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/Image%20Matters-%20Visually%20modeling%20user%20behaviors%20using%20Advanced%20Model%20Server.pdf)
阿里提出引入商品图像特征的(Deep Image CTR Model)CTR预估模型,并介绍其分布式机器学习框架AMS -* `New!` [Deep Interest Network for Click-Through Rate Prediction.pdf](https://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/Deep%20Interest%20Network%20for%20Click-Through%20Rate%20Prediction.pdf)
-阿里提出的深度兴趣网络(Deep Interest Network)CTR预估模型 +* `New!` [Deep Interest Evolution Network for Click-Through Rate Prediction.pdf](https://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/%5BDIEN%5DDeep%20Interest%20Evolution%20Network%20for%20Click-Through%20Rate%20Prediction.pdf)
+阿里提出的深度兴趣网络(Deep Interest Network)最新改进DIEN **其他相关资源** * [张伟楠的RTB Papers列表](https://github.com/wnzhang/rtb-papers)
@@ -37,15 +37,16 @@ Google的深度学习自动调参框架Vizier ### CTR Prediction 作为计算广告的核心,CTR预估永远是研究的热点,下面每一篇都是非常流行的文章,推荐逐一精读 * [Deep Crossing- Web-Scale Modeling without Manually Crafted Combinatorial Features.pdf](https://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/Deep%20Crossing-%20Web-Scale%20Modeling%20without%20Manually%20Crafted%20Combinatorial%20Features.pdf)
+* [[DIEN]Deep Interest Evolution Network for Click-Through Rate Prediction.pdf](https://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/%5BDIEN%5DDeep%20Interest%20Evolution%20Network%20for%20Click-Through%20Rate%20Prediction.pdf)
* [Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction.pdf](https://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/Learning%20Piece-wise%20Linear%20Models%20from%20Large%20Scale%20Data%20for%20Ad%20Click%20Prediction.pdf)
阿里提出的Large Scale Piece-wise Linear Model (LS-PLM) CTR预估模型 * [[GBDT+LR]Practical Lessons from Predicting Clicks on Ads at Facebook.pdf](https://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/%5BGBDT%2BLR%5DPractical%20Lessons%20from%20Predicting%20Clicks%20on%20Ads%20at%20Facebook.pdf)
* [[FNN]Deep Learning over Multi-field Categorical Data.pdf](https://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/%5BFNN%5DDeep%20Learning%20over%20Multi-field%20Categorical%20Data.pdf)
* [Entire Space Multi-Task Model_ An Effective Approach for Estimating Post-Click Conversion Rate.pdf](https://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/Entire%20Space%20Multi-Task%20Model_%20An%20Effective%20Approach%20for%20Estimating%20Post-Click%20Conversion%20Rate.pdf)
-* [Deep Interest Network for Click-Through Rate Prediction.pdf](https://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/Deep%20Interest%20Network%20for%20Click-Through%20Rate%20Prediction.pdf)
* [Bid-aware Gradient Descent for Unbiased Learning with Censored Data in Display Advertising.pdf](https://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/Bid-aware%20Gradient%20Descent%20for%20Unbiased%20Learning%20with%20Censored%20Data%20in%20Display%20Advertising.pdf)
RTB 中训练 CTR 模型数据集是赢得出价的广告,预测时的样本却是所有候选的广告,也就是训练集和测试集的分布不一致,这篇文章就是要消除这样的 bias * [[Multi-Task]An Overview of Multi-Task Learning in Deep Neural Networks.pdf](https://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/%5BMulti-Task%5DAn%20Overview%20of%20Multi-Task%20Learning%20in%20Deep%20Neural%20Networks.pdf)
+* [[DIN]Deep Interest Network for Click-Through Rate Prediction.pdf](https://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/%5BDIN%5DDeep%20Interest%20Network%20for%20Click-Through%20Rate%20Prediction.pdf)
* [Ad Click Prediction a View from the Trenches.pdf](https://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/Ad%20Click%20Prediction%20a%20View%20from%20the%20Trenches.pdf)
Google大名鼎鼎的用FTRL解决CTR在线预估的工程文章,非常经典。 * [[PNN]Product-based Neural Networks for User Response Prediction.pdf](https://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/%5BPNN%5DProduct-based%20Neural%20Networks%20for%20User%20Response%20Prediction.pdf)
@@ -129,29 +130,28 @@ PID控制的经典教程 * [Real-Time Bidding by Reinforcement Learning in Display Advertising.pdf](https://github.com/wzhe06/Ad-papers/blob/master/Bidding%20Strategy/Real-Time%20Bidding%20by%20Reinforcement%20Learning%20in%20Display%20Advertising.pdf)
* [Combining Powers of Two Predictors in Optimizing Real-Time Bidding Strategy under Constrained Budget.pdf](https://github.com/wzhe06/Ad-papers/blob/master/Bidding%20Strategy/Combining%20Powers%20of%20Two%20Predictors%20in%20Optimizing%20Real-Time%20Bidding%20Strategy%20under%20Constrained%20Budget.pdf)
国立台湾大学的文章,介绍一种基于流量选择的计算广告竞价方法,有别于传统的CTR CPC的方法,我在实践中尝试过该方法,非常有效 +* [Optimized Cost per Click in Taobao Display Advertising.pdf](https://github.com/wzhe06/Ad-papers/blob/master/Bidding%20Strategy/Optimized%20Cost%20per%20Click%20in%20Taobao%20Display%20Advertising.pdf)
+淘宝搜索广告中 OCPC 模式的出价策略
* [Real-Time Bidding Algorithms for Performance-Based Display Ad Allocation.pdf](https://github.com/wzhe06/Ad-papers/blob/master/Bidding%20Strategy/Real-Time%20Bidding%20Algorithms%20for%20Performance-Based%20Display%20Ad%20Allocation.pdf)
微软的一篇基于PID反馈控制的与效果相关的竞价算法 * [Deep Reinforcement Learning for Sponsored Search Real-time Bidding.pdf](https://github.com/wzhe06/Ad-papers/blob/master/Bidding%20Strategy/Deep%20Reinforcement%20Learning%20for%20Sponsored%20Search%20Real-time%20Bidding.pdf)
阿里妈妈搜索广告团队的论文,通过 Reinforcement Learning 探索实时出价问题
-* [Optimized Cost per Click in Taobao Display Advertising.pdf](https://github.com/wzhe06/Ad-papers/blob/master/Bidding%20Strategy/Optimized%20Cost%20per%20Click%20in%20Taobao%20Display%20Advertising.pdf)
-淘宝搜索广告中 OCPC 模式的出价策略
- ### Computational Advertising Architect 广告系统的架构问题 -* [Parameter Server for Distributed Machine Learning.pdf](https://github.com/wzhe06/Ad-papers/blob/master/Computational%20Advertising%20Architect/Parameter%20Server%20for%20Distributed%20Machine%20Learning.pdf)
* [[TensorFlow Whitepaper]TensorFlow- Large-Scale Machine Learning on Heterogeneous Distributed Systems.pdf](https://github.com/wzhe06/Ad-papers/blob/master/Computational%20Advertising%20Architect/%5BTensorFlow%20Whitepaper%5DTensorFlow-%20Large-Scale%20Machine%20Learning%20on%20Heterogeneous%20Distributed%20Systems.pdf)
* [大数据下的广告排序技术及实践.pdf](https://github.com/wzhe06/Ad-papers/blob/master/Computational%20Advertising%20Architect/%E5%A4%A7%E6%95%B0%E6%8D%AE%E4%B8%8B%E7%9A%84%E5%B9%BF%E5%91%8A%E6%8E%92%E5%BA%8F%E6%8A%80%E6%9C%AF%E5%8F%8A%E5%AE%9E%E8%B7%B5.pdf)
阿里妈妈的一篇广告排序问题的ppt,模型、训练、评估都有涉及,很有工程价值 * [美团机器学习 吃喝玩乐中的算法问题.pdf](https://github.com/wzhe06/Ad-papers/blob/master/Computational%20Advertising%20Architect/%E7%BE%8E%E5%9B%A2%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%20%E5%90%83%E5%96%9D%E7%8E%A9%E4%B9%90%E4%B8%AD%E7%9A%84%E7%AE%97%E6%B3%95%E9%97%AE%E9%A2%98.pdf)
美团王栋博士的一篇关于美团机器学习相关问题的介绍,介绍的比较全但比较粗浅,可以借此了解美团的一些机器学习问题 +* [[Parameter Server]Scaling Distributed Machine Learning with the Parameter Server.pdf](https://github.com/wzhe06/Ad-papers/blob/master/Computational%20Advertising%20Architect/%5BParameter%20Server%5DScaling%20Distributed%20Machine%20Learning%20with%20the%20Parameter%20Server.pdf)
* [Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting.pdf](https://github.com/wzhe06/Ad-papers/blob/master/Computational%20Advertising%20Architect/Display%20Advertising%20with%20Real-Time%20Bidding%20%28RTB%29%20and%20Behavioural%20Targeting.pdf)
张伟楠博士的RTB过程所有相关算法的书,全而精,非常棒 * [A Comparison of Distributed Machine Learning Platforms.pdf](https://github.com/wzhe06/Ad-papers/blob/master/Computational%20Advertising%20Architect/A%20Comparison%20of%20Distributed%20Machine%20Learning%20Platforms.pdf)
* [Efficient Query Evaluation using a Two-Level Retrieval Process.pdf](https://github.com/wzhe06/Ad-papers/blob/master/Computational%20Advertising%20Architect/Efficient%20Query%20Evaluation%20using%20a%20Two-Level%20Retrieval%20Process.pdf)
搜索广告中经典的搜索算法 Wand(Weak AND) * [[TensorFlow Whitepaper]TensorFlow- A System for Large-Scale Machine Learning.pdf](https://github.com/wzhe06/Ad-papers/blob/master/Computational%20Advertising%20Architect/%5BTensorFlow%20Whitepaper%5DTensorFlow-%20A%20System%20for%20Large-Scale%20Machine%20Learning.pdf)
-* [Scaling Distributed Machine Learning with the Parameter Server.pdf](https://github.com/wzhe06/Ad-papers/blob/master/Computational%20Advertising%20Architect/Scaling%20Distributed%20Machine%20Learning%20with%20the%20Parameter%20Server.pdf)
+* [[Parameter Server]Parameter Server for Distributed Machine Learning.pdf](https://github.com/wzhe06/Ad-papers/blob/master/Computational%20Advertising%20Architect/%5BParameter%20Server%5DParameter%20Server%20for%20Distributed%20Machine%20Learning.pdf)
* [Overlapping Experiment Infrastructure More, Better, Faster Experimentation.pdf](https://github.com/wzhe06/Ad-papers/blob/master/Computational%20Advertising%20Architect/Overlapping%20Experiment%20Infrastructure%20More%2C%20Better%2C%20Faster%20Experimentation.pdf)
Google 一篇关于 A/B 测试框架的论文,涉及到如何切分流量以同时进行多个 A/B 测试,工程性很强

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