update comment and add requirements.txt

fluid/recommendation/ctr/README.cn.md

@@ -14,6 +14,13 @@
 }
 ```
 
+## 运行环境
+需要先安装PaddlePaddle Fluid，然后运行：
+
+```shell
+pip install -r requirements.txt
+```
+
 ## 数据集
 本文使用的是Kaggle公司举办的[展示广告竞赛](https://www.kaggle.com/c/criteo-display-ad-challenge/)中所使用的Criteo数据集。
 
@@ -27,7 +34,6 @@ cd data && ./download.sh && cd ..
 ## 模型
 本例子只实现了DeepFM论文中介绍的模型的DNN部分，DeepFM会在其他例子中给出。
 
-```
 
 ## 数据准备
 处理原始数据集，整型特征使用min-max归一化方法规范到[0, 1]，类别类特征使用了one-hot编码。原始数据集分割成两部分：90%用于训练，其他10%用于训练过程中的验证。

fluid/recommendation/ctr/README.md

@@ -19,6 +19,13 @@ both low order and high order feature interactions. For details of the
 factorization machines, please refer to the paper [factorization
 machines](https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf)
 
+## Environment
+You should install PaddlePaddle Fluid first, and run:
+
+```shell
+pip install -r requirements.txt
+```
+
 ## Dataset
 This example uses Criteo dataset which was used for the [Display Advertising
 Challenge](https://www.kaggle.com/c/criteo-display-ad-challenge/)

fluid/recommendation/ctr/preprocess.py

@@ -89,8 +89,8 @@ class ContinuousFeatureGenerator:
 @click.option("--outdir", type=str, help="Path to save the processed data")
 def preprocess(datadir, outdir):
     """
-    All the 13 integer features are normalzied to continous values and these
-    continous features are combined into one vecotr with dimension 13.
+    All 13 integer features are normalized to continuous values and these continuous
+    features are combined into one vector with dimension of 13.
 
     Each of the 26 categorical features are one-hot encoded and all the one-hot
     vectors are combined into one sparse binary vector.

fluid/recommendation/ctr/requirements.txt

+click