upload DIEN paper

README.md

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 # 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)
 
 **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) <br />
 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) <br />
-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) <br />
+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)<br />
@@ -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) <br />
+* [[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) <br />
 * [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) <br />
 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) <br />
 * [[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) <br />
 * [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) <br />
-* [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) <br />
 * [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) <br />
 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) <br />
+* [[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) <br />
 * [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) <br />
 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) <br />
@@ -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) <br />
 * [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) <br />
 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) <br />
+淘宝搜索广告中 OCPC 模式的出价策略<br />
 * [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) <br />
 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) <br />
 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<br />
-* [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) <br />
-淘宝搜索广告中 OCPC 模式的出价策略<br />
-
 
 ### 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) <br />
 * [[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) <br />
 * [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) <br />
 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) <br />
 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) <br />
 * [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) <br />
 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) <br />
 * [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) <br />
 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) <br />
-* [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) <br />
+* [[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) <br />
 * [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) <br />
 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