update some introductions

README.md

@@ -34,6 +34,7 @@ Online Optimization，Parallel SGD，FTRL等优化方法，实用并且能够给
 CTR预估模型相关问题，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 />
 * [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
 * [[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 />
@@ -44,6 +45,7 @@ RTB 中训练 CTR 模型数据集是赢得出价的广告，预测时的样本
 * [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。
 * [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 Advanced Model Server (AMS)
 * [[DeepFM]- A Factorization-Machine based Neural Network for CTR Prediction.pdf](https://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/%5BDeepFM%5D-%20A%20Factorization-Machine%20based%20Neural%20Network%20for%20CTR%20Prediction.pdf) <br />
 * [Logistic Regression in Rare Events Data.pdf](https://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/Logistic%20Regression%20in%20Rare%20Events%20Data.pdf) <br />
 样本稀少情况下的LR模型训练，讲的比较细