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Copy file name to clipboardexpand all lines: README.md
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@@ -45,6 +45,12 @@ CTR预估模型相关问题
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*[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 />
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*[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 />
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*[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 />
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*[Wide & Deep Learning for Recommender Systems](https://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/Wide%20%26%20Deep%20Learning%20for%20Recommender%20Systems.pdf) <br />
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Google 的 Wide & Deep 模型,论文将模型用于推荐系统中,但也可用于 CTR 预估中
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*[Deep Learning over Multi-field Categorical Data](https://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/Deep%20Learning%20over%20Multi-field%20Categorical%20Data.pdf) <br />
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张伟楠博士的论文,提出了 FNN 模型,类似 Wide & Deep 的 Deep 部分,亮点在于用 FM 预训练的隐向量初始化 embedding 层
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*[Product-based Neural Networks for User Response Prediction](https://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/Product-based%20Neural%20Networks%20for%20User%20Response%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 />
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Google大名鼎鼎的用FTRL解决CTR在线预估的工程文章,非常经典。
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*[DeepFM- A Factorization-Machine based Neural Network for CTR Prediction.pdf](https://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/DeepFM-%20A%20Factorization-Machine%20based%20Neural%20Network%20for%20CTR%20Prediction.pdf) <br />
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张伟楠博士的RTB过程所有相关算法的书,全而精,非常棒
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*[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 />
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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*[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 />
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