修改一些措辞

youtube_recall/README.cn.md

@@ -34,6 +34,8 @@ Figure 1. 推荐系统框图
 - 召回模型: 输入用户的历史行为，从大规模的内容库中获得一个小集合(百级别)。召回出的视频与用户高度相关。一个用户是用其历史点击过的视频，搜索过的关键词，和人口统计相关的特征来表征。
 - 排序模型: 采用更精细的特征计算得到排序分，对召回得到的候选集合中的视频进行排序。
 
+本文主要详细介绍了召回模型的原理与使用。
+
 ## 召回模型简介
 该推荐问题可以被建模成一个"超大规模多分类"问题。即在时刻![](https://www.zhihu.com/equation?tex=t)，为用户![](https://www.zhihu.com/equation?tex=U)(已知上下文信息![](https://www.zhihu.com/equation?tex=C))在视频库![](https://www.zhihu.com/equation?tex=V)中预测出观看视频![](https://www.zhihu.com/equation?tex=i)的类别，
 
@@ -61,7 +63,7 @@ Figure 2. 召回模型网络结构
 例如:
 USER_ID_15  上海市  上海市    VIDEO_42:CATEGORY_9:TAG115;VIDEO_43:CATEGORY_9:TAG116_TAG115;VIDEO_44:CATEGORY_2:TAG117_TAG71  GO T5
 ```
-运行以下命令可解压样本数据。
+在youtube_recall目录下运行以下命令（下同），可以解压样本数据。
 ```
 cd data
 tar -zxvf data.tar
@@ -173,7 +175,10 @@ def _build_embedding_layer(self):
 ```
 
 ### 隐层
-对原paper中做了改进，历史用户点击视频序列，经过embedding后，不再是加权求平均。而是连接lstm层，将用户点击的先后次序纳入模型，再在时间序列上做最大池化，得到定长的向量表示，从而使模型学习到与点击时序相关的隐藏信息。考虑到数据规模与训练性能，这边只用了两个Relu层，也有不错的效果。
+本文对\[[原论文](#参考文献)\](Covington, Paul, Jay Adams, and Emre Sargin. "Deep neural networks for youtube recommendations." Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 2016.)中的模型做了如下改进：
+- 历史用户点击的视频序列，经过embedding之后，不再使用加权求平均，而是使用lstm序列模型。本文将用户点击的先后次序纳入模型中，然后在时间序列上做最大池化，得到定长向量表示，从而使模型学习到与点击时序相关的隐藏信息。
+- 考虑到数据规模与训练性能，本文只用了两个Relu层，也有很不错的效果。
+
 ```python
 self._rnn_cell = paddle.networks.simple_lstm(
             input=self._history_clicked_items_emb, size=64)

youtube_recall/README.md

@@ -16,6 +16,8 @@ The system is comprised of two neural networks: one for candidate generation and
 - The candidate generation network: It takes events from the user's YouTube activity history as input and retrieves a small subset(hundreds) of videos, highly relevant to the user, from a large corpus. The similarity between users is expressed in terms of coarse features such as IDs of video watches, search query tokens and demographics.
 - The ranking network: It accomplishes this task by assigning a score to each video according to a desired objective function using a rich set of features describing the video and user.
 
+This markdown describes the principle and use of the candidate generation network in detail.
+
 ## Candidate Generation
 Here, candidate generation is modeled as extreme multiclass classification where the prediction problem becomes accurately classifying a specific video watch ![](https://www.zhihu.com/equation?tex=%5Comega_t) at time ![](https://www.zhihu.com/equation?tex=t) among millions of video ![](https://www.zhihu.com/equation?tex=i) (classes) from a corpus ![](https://www.zhihu.com/equation?tex=V) based on user ![](https://www.zhihu.com/equation?tex=U) and context ![](https://www.zhihu.com/equation?tex=C),
 
@@ -48,7 +50,7 @@ video-id:category:tag1_tag2_tag3_...tagM
 For example:
 USER_ID_15  Shanghai  Shanghai    VIDEO_42:CATEGORY_9:TAG115;VIDEO_43:CATEGORY_9:TAG116_TAG115;VIDEO_44:CATEGORY_2:TAG117_TAG71  GO T5
 ```
-Run this code to prepare the sample data.
+Run this code in `youtube_recall` directory (the same below) to prepare the sample data.
 ```
 cd data
 tar -zxvf data.tar
@@ -160,7 +162,9 @@ def _build_embedding_layer(self):
 ```
 
 ### Hiddern layer
-Here improves the original networks in \[[1](#References)\] by modifying that the embeddings of video watches are not simply averaged but are connected to a LSTM layer with max temporal pooling instead, so that the deep sequential information related to user interests can be learned well. Considering data scale and efficiency of training, only two ReLU layers are applied, which also leads to good performance.
+Here improves the original networks in \[[Original Paper](#References)\](Covington, Paul, Jay Adams, and Emre Sargin. "Deep neural networks for youtube recommendations." Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 2016.)
+- By modifying that the embeddings of video watches are not simply averaged but are connected to a LSTM layer with max temporal pooling instead, so that the deep sequential information related to user interests can be learned well.
+- Considering data scale and efficiency of training, only two ReLU layers are applied, which also leads to good performance.
 
 ```python
 self._rnn_cell = paddle.networks.simple_lstm(input=self._history_clicked_items_emb, size=64)