Add README.md

7445b6ed · Yu Yang · 70bc4acb · 7445b6ed · 7445b6ed · 7445b6ed
4 changed file
--- a/recommender_system/.gitignore
+++ b/recommender_system/.gitignore
 .idea
 .ipynb_checkpoints
-params.pkl
--- a/recommender_system/api_train.ipynb
+++ b/recommender_system/api_train.ipynb
@@ -98,99 +98,10 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
-   "source": [
+    "deletable": true,
-    "# 个性化推荐\n",
+    "editable": true
-    "\n",
+   },
-    "本教程源代码目录在[book/recommender_system](https://github.com/PaddlePaddle/book/tree/develop/recommender_system)， 初次使用请参考PaddlePaddle[安装教程](http://www.paddlepaddle.org/doc_cn/build_and_install/index.html)。\n",
-    "\n",
-    "## 背景介绍\n",
-    "\n",
-    "在网络技术不断发展和电子商务规模不断扩大的背景下，商品数量和种类快速增长，用户需要花费大量时间才能找到自己想买的商品，这就是信息超载问题。为了解决这个难题，推荐系统（Recommender System）应运而生。\n",
-    "\n",
-    "个性化推荐系统是信息过滤系统（Information Filtering System）的子集，它可以用在很多领域，如电影、音乐、电商和 Feed 流推荐等。推荐系统通过分析、挖掘用户行为，发现用户的个性化需求与兴趣特点，将用户可能感兴趣的信息或商品推荐给用户。与搜索引擎不同，推荐系统不需要用户准确地描述出自己的需求，而是根据分析历史行为建模，主动提供满足用户兴趣和需求的信息。\n",
-    "\n",
-    "传统的推荐系统方法主要有：\n",
-    "\n",
-    "- 协同过滤推荐（Collaborative Filtering Recommendation）：该方法收集分析用户历史行为、活动、偏好，计算一个用户与其他用户的相似度，利用目标用户的相似用户对商品评价的加权评价值，来预测目标用户对特定商品的喜好程度。优点是可以给用户推荐未浏览过的新产品；缺点是对于没有任何行为的新用户存在冷启动的问题，同时也存在用户与商品之间的交互数据不够多造成的稀疏问题，会导致模型难以找到相近用户。\n",
-    "- 基于内容过滤推荐[[1](#参考文献)]（Content-based Filtering Recommendation）：该方法利用商品的内容描述，抽象出有意义的特征，通过计算用户的兴趣和商品描述之间的相似度，来给用户做推荐。优点是简单直接，不需要依据其他用户对商品的评价，而是通过商品属性进行商品相似度度量，从而推荐给用户所感兴趣商品的相似商品；缺点是对于没有任何行为的新用户同样存在冷启动的问题。\n",
-    "- 组合推荐[[2](#参考文献)]（Hybrid Recommendation）：运用不同的输入和技术共同进行推荐，以弥补各自推荐技术的缺点。\n",
-    "\n",
-    "其中协同过滤是应用最广泛的技术之一，它又可以分为多个子类：基于用户 （User-Based）的推荐[[3](#参考文献)] 、基于物品（Item-Based）的推荐[[4](#参考文献)]、基于社交网络关系（Social-Based）的推荐[[5](#参考文献)]、基于模型（Model-based）的推荐等。1994年明尼苏达大学推出的GroupLens系统[[3](#参考文献)]一般被认为是推荐系统成为一个相对独立的研究方向的标志。该系统首次提出了基于协同过滤来完成推荐任务的思想，此后，基于该模型的协同过滤推荐引领了推荐系统十几年的发展方向。\n",
-    "\n",
-    "深度学习具有优秀的自动提取特征的能力，能够学习多层次的抽象特征表示，并对异质或跨域的内容信息进行学习，可以一定程度上处理推荐系统冷启动问题[[6](#参考文献)]。本教程主要介绍个性化推荐的深度学习模型，以及如何使用PaddlePaddle实现模型。\n",
-    "\n",
-    "## 效果展示\n",
-    "\n",
-    "我们使用包含用户信息、电影信息与电影评分的数据集作为个性化推荐的应用场景。当我们训练好模型后，只需要输入对应的用户ID和电影ID，就可以得出一个匹配的分数（范围[1,5]，分数越高视为兴趣越大），然后根据所有电影的推荐得分排序，推荐给用户可能感兴趣的电影。\n",
-    "\n",
-    "```\n",
-    "Input movie_id: 1962\n",
-    "Input user_id: 1\n",
-    "Prediction Score is 4.25\n",
-    "```\n",
-    "\n",
-    "## 模型概览\n",
-    "\n",
-    "本章中，我们首先介绍YouTube的视频推荐系统[[7](#参考文献)]，然后介绍我们实现的融合推荐模型。\n",
-    "\n",
-    "### YouTube的深度神经网络推荐系统\n",
-    "\n",
-    "YouTube是世界上最大的视频上传、分享和发现网站，YouTube推荐系统为超过10亿用户从不断增长的视频库中推荐个性化的内容。整个系统由两个神经网络组成：候选生成网络和排序网络。候选生成网络从百万量级的视频库中生成上百个候选，排序网络对候选进行打分排序，输出排名最高的数十个结果。系统结构如图1所示：\n",
-    "\n",
-    "<p align=\"center\">\n",
-    "<img src=\"image/YouTube_Overview.png\" width=\"70%\" ><br/>\n",
-    "图1. YouTube 推荐系统结构\n",
-    "</p>\n",
-    "\n",
-    "#### 候选生成网络（Candidate Generation Network）\n",
-    "\n",
-    "候选生成网络将推荐问题建模为一个类别数极大的多类分类问题：对于一个Youtube用户，使用其观看历史（视频ID）、搜索词记录（search tokens）、人口学信息（如地理位置、用户登录设备）、二值特征（如性别，是否登录）和连续特征（如用户年龄）等，对视频库中所有视频进行多分类，得到每一类别的分类结果（即每一个视频的推荐概率），最终输出概率较高的几百个视频。\n",
-    "\n",
-    "首先，将观看历史及搜索词记录这类历史信息，映射为向量后取平均值得到定长表示；同时，输入人口学特征以优化新用户的推荐效果，并将二值特征和连续特征归一化处理到[0, 1]范围。接下来，将所有特征表示拼接为一个向量，并输入给非线形多层感知器（MLP，详见[识别数字](https://github.com/PaddlePaddle/book/blob/develop/recognize_digits/README.md)教程）处理。最后，训练时将MLP的输出给softmax做分类，预测时计算用户的综合特征（MLP的输出）与所有视频的相似度，取得分最高的$k$个作为候选生成网络的筛选结果。图2显示了候选生成网络结构。\n",
-    "\n",
-    "<p align=\"center\">\n",
-    "<img src=\"image/Deep_candidate_generation_model_architecture.png\" width=\"70%\" ><br/>\n",
-    "图2. 候选生成网络结构\n",
-    "</p>\n",
-    "\n",
-    "对于一个用户$U$，预测此刻用户要观看的视频$\\omega$为视频$i$的概率公式为：\n",
-    "\n",
-    "$$P(\\omega=i|u)=\\frac{e^{v_{i}u}}{\\sum_{j \\in V}e^{v_{j}u}}$$\n",
-    "\n",
-    "其中$u$为用户$U$的特征表示，$V$为视频库集合，$v_i$为视频库中第$i$个视频的特征表示。$u$和$v_i$为长度相等的向量，两者点积可以通过全连接层实现。\n",
-    "\n",
-    "考虑到softmax分类的类别数非常多，为了保证一定的计算效率：1）训练阶段，使用负样本类别采样将实际计算的类别数缩小至数千；2）推荐（预测）阶段，忽略softmax的归一化计算（不影响结果），将类别打分问题简化为点积（dot product）空间中的最近邻（nearest neighbor）搜索问题，取与$u$最近的$k$个视频作为生成的候选。\n",
-    "\n",
-    "#### 排序网络（Ranking Network）\n",
-    "排序网络的结构类似于候选生成网络，但是它的目标是对候选进行更细致的打分排序。和传统广告排序中的特征抽取方法类似，这里也构造了大量的用于视频排序的相关特征（如视频 ID、上次观看时间等）。这些特征的处理方式和候选生成网络类似，不同之处是排序网络的顶部是一个加权逻辑回归（weighted logistic regression），它对所有候选视频进行打分，从高到底排序后将分数较高的一些视频返回给用户。\n",
-    "\n",
-    "### 融合推荐模型\n",
-    "\n",
-    "在下文的电影推荐系统中：\n",
-    "\n",
-    "1. 首先，使用用户特征和电影特征作为神经网络的输入，其中：\n",
-    "\n",
-    "   - 用户特征融合了四个属性信息，分别是用户ID、性别、职业和年龄。\n",
-    "\n",
-    "   - 电影特征融合了三个属性信息，分别是电影ID、电影类型ID和电影名称。\n",
-    "\n",
-    "2. 对用户特征，将用户ID映射为维度大小为256的向量表示，输入全连接层，并对其他三个属性也做类似的处理。然后将四个属性的特征表示分别全连接并相加。\n",
-    "\n",
-    "3. 对电影特征，将电影ID以类似用户ID的方式进行处理，电影类型ID以向量的形式直接输入全连接层，电影名称用文本卷积神经网络（详见[第5章](https://github.com/PaddlePaddle/book/blob/develop/understand_sentiment/README.md)）得到其定长向量表示。然后将三个属性的特征表示分别全连接并相加。\n",
-    "\n",
-    "4. 得到用户和电影的向量表示后，计算二者的余弦相似度作为推荐系统的打分。最后，用该相似度打分和用户真实打分的差异的平方作为该回归模型的损失函数。\n",
-    "\n",
-    "<p align=\"center\">\n",
-    "\n",
-    "<img src=\"image/rec_regression_network.png\" width=\"90%\" ><br/>\n",
-    "图3. 融合推荐模型 \n",
-    "</p> "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
   "source": [
    "## 数据准备\n",
    "\n",
@@ -205,7 +116,9 @@
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
   },
   "outputs": [],
   "source": [
@@ -217,7 +130,9 @@
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
   },
   "outputs": [],
   "source": [
@@ -227,7 +142,10 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
   "source": [
    "在原始数据中包含电影的特征数据，用户的特征数据，和用户对电影的评分。\n",
    "\n",
@@ -238,7 +156,9 @@
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
   },
   "outputs": [
    {
@@ -256,7 +176,10 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
   "source": [
    "这表示，电影的id是1，标题是《Toy Story》，该电影被分为到三个类别中。这三个类别是动画，儿童，喜剧。"
   ]
@@ -265,7 +188,9 @@
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
   },
   "outputs": [
    {
@@ -283,7 +208,10 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
   "source": [
    "这表示，该用户ID是1，女性，年龄比18岁还年轻。职业ID是10。\n",
    "\n",
@@ -323,7 +251,10 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
   "source": [
    "而对于每一条训练/测试数据，均为 <用户特征> + <电影特征> + 评分。\n",
    "\n",
@@ -334,7 +265,9 @@
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
   },
   "outputs": [
    {
@@ -355,14 +288,20 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
   "source": [
    "即用户1对电影1193的评价为5分。"
   ]
  },
  {
   "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
   "source": [
    "## 模型配置说明\n",
    "\n",
@@ -373,7 +312,9 @@
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
   },
   "outputs": [],
   "source": [
@@ -402,7 +343,10 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
   "source": [
    "如上述代码所示，对于每个用户，我们输入4维特征。其中包括`user_id`,`gender_id`,`age_id`,`job_id`。这几维特征均是简单的整数值。为了后续神经网络处理这些特征方便，我们借鉴NLP中的语言模型，将这几维离散的整数值，变换成embedding取出。分别形成`usr_emb`, `usr_gender_emb`, `usr_age_emb`, `usr_job_emb`。"
   ]
@@ -411,7 +355,9 @@
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
   },
   "outputs": [],
   "source": [
@@ -423,14 +369,20 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
   "source": [
    "然后，我们对于所有的用户特征，均输入到一个全连接层(fc)中。将所有特征融合为一个200维度的特征。"
   ]
  },
  {
   "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
   "source": [
    "进而，我们对每一个电影特征做类似的变换，网络配置为:"
   ]
@@ -439,7 +391,9 @@
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
   },
   "outputs": [],
   "source": [
@@ -473,7 +427,10 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
   "source": [
    "电影ID和电影类型分别映射到其对应的特征隐层。对于电影标题名称(title)，一个ID序列表示的词语序列，在输入卷积层后，将得到每个时间窗口的特征（序列特征），然后通过在时间维度降采样得到固定维度的特征，整个过程在text_conv_pool实现。\n",
    "\n",
@@ -484,7 +441,9 @@
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
   },
   "outputs": [],
   "source": [
@@ -493,7 +452,10 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
   "source": [
    "进而，我们使用余弦相似度计算用户特征与电影特征的相似性。并将这个相似性拟合(回归)到用户评分上。"
   ]
@@ -502,7 +464,9 @@
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
   },
   "outputs": [],
   "source": [
@@ -514,14 +478,20 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
   "source": [
    "至此，我们的优化目标就是这个网络配置中的`cost`了。"
   ]
  },
  {
   "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
   "source": [
    "## 训练模型\n",
    "\n",
@@ -533,15 +503,17 @@
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
-      "[INFO 2017-03-02 17:44:56,684 networks.py:1472] The input order is [user_id, gender_id, age_id, job_id, movie_id, category_id, movie_title, score]\n",
+      "[INFO 2017-03-06 17:12:13,284 networks.py:1472] The input order is [user_id, gender_id, age_id, job_id, movie_id, category_id, movie_title, score]\n",
-      "[INFO 2017-03-02 17:44:56,685 networks.py:1478] The output order is [__regression_cost_0__]\n"
+      "[INFO 2017-03-06 17:12:13,287 networks.py:1478] The output order is [__regression_cost_0__]\n"
     ]
    }
   ],
@@ -551,7 +523,10 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
   "source": [
    "`parameters`是模型的所有参数集合。他是一个python的dict。我们可以查看到这个网络中的所有参数名称。因为之前定义模型的时候，我们没有指定参数名称，这里参数名称是自动生成的。当然，我们也可以指定每一个参数名称，方便日后维护。"
   ]
@@ -560,7 +535,9 @@
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
   },
   "outputs": [
    {
@@ -577,7 +554,10 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
   "source": [
    "### 构造训练(trainer)\n",
    "\n",
@@ -588,15 +568,17 @@
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
-      "[INFO 2017-03-02 17:44:56,753 networks.py:1472] The input order is [user_id, gender_id, age_id, job_id, movie_id, category_id, movie_title, score]\n",
+      "[INFO 2017-03-06 17:12:13,378 networks.py:1472] The input order is [user_id, gender_id, age_id, job_id, movie_id, category_id, movie_title, score]\n",
-      "[INFO 2017-03-02 17:44:56,763 networks.py:1478] The output order is [__regression_cost_0__]\n"
+      "[INFO 2017-03-06 17:12:13,379 networks.py:1478] The output order is [__regression_cost_0__]\n"
     ]
    }
   ],
@@ -607,7 +589,10 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
   "source": [
    "### 训练\n",
    "\n",
@@ -624,14 +609,16 @@
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
   },
   "outputs": [
    {
     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUyJ4QHccxykRPKA7juOUCL8B+O/KNKrXoUUAAAAASUVORK5CYII=\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOoQI6EEQBB1CBPQgCIIO4X9iGnorp+WAJQAAAABJRU5ErkJggg==\n",
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10c0eb850>"
+       "<matplotlib.figure.Figure at 0x119bae4d0>"
      ]
     },
     "metadata": {},
@@ -640,7 +627,7 @@
    {
     "data": {
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10c0eb850>"
+       "<matplotlib.figure.Figure at 0x119bae4d0>"
      ]
     },
     "metadata": {},
@@ -654,7 +641,7 @@
    "from IPython import display\n",
    "import cPickle\n",
    "\n",
-    "reader_dict = {\n",
+    "feeding = {\n",
    "    'user_id': 0,\n",
    "    'gender_id': 1,\n",
    "    'age_id': 2,\n",
@@ -681,7 +668,7 @@
    "            train_costs[1].append(event.cost)\n",
    "            \n",
    "        if step % 1000 == 0: # every 1000 batches, record a test cost\n",
-    "            result = trainer.test(reader=paddle.reader.batched(\n",
+    "            result = trainer.test(reader=paddle.batch(\n",
    "                  paddle.dataset.movielens.test(), batch_size=256))\n",
    "            test_costs[0].append(step)\n",
    "            test_costs[1].append(result.cost)\n",
@@ -696,18 +683,21 @@
    "        step += 1\n",
    "\n",
    "trainer.train(\n",
-    "    reader=paddle.reader.batched(\n",
+    "    reader=paddle.batch(\n",
    "            paddle.reader.shuffle(\n",
    "            paddle.dataset.movielens.train(), buf_size=8192),\n",
    "                            batch_size=256),\n",
    "    event_handler=event_handler,\n",
-    "    reader_dict=reader_dict,\n",
+    "    feeding=feeding,\n",
    "    num_passes=2)"
   ]
  },
  {
   "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
   "source": [
    "## 应用模型\n",
    "\n",
@@ -718,22 +708,24 @@
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
-      "[INFO 2017-03-02 17:49:59,181 networks.py:1472] The input order is [user_id, gender_id, age_id, job_id, movie_id, category_id, movie_title]\n",
+      "[INFO 2017-03-06 17:17:08,132 networks.py:1472] The input order is [user_id, gender_id, age_id, job_id, movie_id, category_id, movie_title]\n",
-      "[INFO 2017-03-02 17:49:59,195 networks.py:1478] The output order is [__cos_sim_0__]\n"
+      "[INFO 2017-03-06 17:17:08,134 networks.py:1478] The output order is [__cos_sim_0__]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "[Predict] User 234 Rating Movie 345 With Score 4.12\n"
+      "[Predict] User 234 Rating Movie 345 With Score 4.16\n"
     ]
    }
   ],
@@ -747,20 +739,20 @@
    "\n",
    "feature = user.value() + movie.value()\n",
    "\n",
-    "def reader():\n",
+    "infer_dict = copy.copy(feeding)\n",
-    "    yield feature\n",
-    "\n",
-    "infer_dict = copy.copy(reader_dict)\n",
    "del infer_dict['score']\n",
    "\n",
-    "prediction = paddle.infer(output=inference, parameters=parameters, reader=paddle.reader.batched(reader, batch_size=32))\n",
+    "prediction = paddle.infer(output=inference, parameters=parameters, input=[feature], feeding=infer_dict)\n",
    "score = (prediction[0][0] + 5.0) / 2\n",
-    "print \"[Predict] User %d Rating Movie %d With Score %.2f\"%(user_id, movie_id, score)\n"
+    "print \"[Predict] User %d Rating Movie %d With Score %.2f\"%(user_id, movie_id, score)"
   ]
  },
  {
   "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
   "source": [
    "## 总结\n",
    "\n",
@@ -779,15 +771,6 @@
    "<br/>\n",
    "<a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\"><img alt=\"知识共享许可协议\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" /></a><br /><span xmlns:dct=\"http://purl.org/dc/terms/\" href=\"http://purl.org/dc/dcmitype/Text\" property=\"dct:title\" rel=\"dct:type\">本教程</span> 由 <a xmlns:cc=\"http://creativecommons.org/ns#\" href=\"http://book.paddlepaddle.org\" property=\"cc:attributionName\" rel=\"cc:attributionURL\">PaddlePaddle</a> 创作，采用 <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">知识共享 署名-非商业性使用-相同方式共享 4.0 国际 许可协议</a>进行许可。\n"
   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
  }
 ],
 "metadata": {

--- a/recommender_system/README.md
+++ b/recommender_system/README.md
@@ -91,278 +91,330 @@ $$P(\omega=i|u)=\frac{e^{v_{i}u}}{\sum_{j \in V}e^{v_{j}u}}$$
 我们以 [MovieLens 百万数据集（ml-1m）](http://files.grouplens.org/datasets/movielens/ml-1m.zip)为例进行介绍。ml-1m 数据集包含了 6,000 位用户对 4,000 部电影的 1,000,000 条评价（评分范围 1~5 分，均为整数），由 GroupLens Research 实验室搜集整理。
-您可以运行 `data/getdata.sh` 下载数据，如果数椐获取成功，您将在目录`data/ml-1m`中看到下面的文件：
+Paddle在API中提供了自动加载数据的模块。数据模块为 `paddle.dataset.movielens`
+```python
+import paddle.v2 as paddle
+paddle.init(use_gpu=False)
 ```
-movies.dat  ratings.dat  users.dat  README 
+```python
+# Run this block to show dataset's documentation
+# help(paddle.dataset.movielens)
 ```
- movies.dat：电影特征数据，格式为`电影ID::电影名称::电影类型`
+在原始数据中包含电影的特征数据，用户的特征数据，和用户对电影的评分。
- ratings.dat：评分数据，格式为`用户ID::电影ID::评分::时间戳`
- users.dat：用户特征数据，格式为`用户ID::性别::年龄::职业::邮编`
- README：数据集的详细描述
-### 数据预处理
+例如，其中某一个电影特征为:
-首先安装 Python 第三方库（推荐使用 Virtualenv）：
-```shell
+```python
-pip install -r data/requirements.txt
+movie_info = paddle.dataset.movielens.movie_info()
+print movie_info.values()[0]
 ```
-其次在预处理`./preprocess.sh`过程中，我们将字段配置文件`data/config.json`转化为meta配置文件`meta_config.json`，并生成对应的meta文件`meta.bin`，以完成数据文件的序列化。然后再将`ratings.dat`分为训练集、测试集两部分，把它们的地址写入`train.list`和`test.list`。
+    <MovieInfo id(1), title(Toy Story ), categories(['Animation', "Children's", 'Comedy'])>
-运行成功后目录`./data` 新增以下文件：
+这表示，电影的id是1，标题是《Toy Story》，该电影被分为到三个类别中。这三个类别是动画，儿童，喜剧。
+```python
+user_info = paddle.dataset.movielens.user_info()
+print user_info.values()[0]
 ```
-meta_config.json  meta.bin  ratings.dat.train  ratings.dat.test  train.list  test.list
+    <UserInfo id(1), gender(F), age(1), job(10)>
+这表示，该用户ID是1，女性，年龄比18岁还年轻。职业ID是10。
+其中，年龄使用下列分布
+*  1:  "Under 18"
+* 18:  "18-24"
+* 25:  "25-34"
+* 35:  "35-44"
+* 45:  "45-49"
+* 50:  "50-55"
+* 56:  "56+"
+职业是从下面几种选项里面选则得出:
+*  0:  "other" or not specified
+*  1:  "academic/educator"
+*  2:  "artist"
+*  3:  "clerical/admin"
+*  4:  "college/grad student"
+*  5:  "customer service"
+*  6:  "doctor/health care"
+*  7:  "executive/managerial"
+*  8:  "farmer"
+*  9:  "homemaker"
+* 10:  "K-12 student"
+* 11:  "lawyer"
+* 12:  "programmer"
+* 13:  "retired"
+* 14:  "sales/marketing"
+* 15:  "scientist"
+* 16:  "self-employed"
+* 17:  "technician/engineer"
+* 18:  "tradesman/craftsman"
+* 19:  "unemployed"
+* 20:  "writer"
+而对于每一条训练/测试数据，均为 <用户特征> + <电影特征> + 评分。
+例如，我们获得第一条训练数据:
+```python
+train_set_creator = paddle.dataset.movielens.train()
+train_sample = next(train_set_creator())
+uid = train_sample[0]
+mov_id = train_sample[len(user_info[uid].value())]
+print "User %s rates Movie %s with Score %s"%(user_info[uid], movie_info[mov_id], train_sample[-1])
 ```
- meta.bin: meta文件是Python的pickle对象， 存储着电影和用户信息。
+    User <UserInfo id(1), gender(F), age(1), job(10)> rates Movie <MovieInfo id(1193), title(One Flew Over the Cuckoo's Nest ), categories(['Drama'])> with Score [5.0]
- meta_config.json: meta配置文件，用来具体描述如何解析数据集中的每一个字段，由字段配置文件生成。
- ratings.dat.train和ratings.dat.test: 训练集和测试集，训练集已经随机打乱。
- train.list和test.list: 训练集和测试集的文件地址列表。
-### 提供数据给 PaddlePaddle
+即用户1对电影1193的评价为5分。
+## 模型配置说明
+下面我们开始根据输入数据的形式配置模型。
-我们使用 Python 接口传递数据给系统，下面 `dataprovider.py` 给出了完整示例。
 ```python
-from paddle.trainer.PyDataProvider2 import *
+uid = paddle.layer.data(
-from common_utils import meta_to_header
+        name='user_id',
+        type=paddle.data_type.integer_value(
-def __list_to_map__(lst):  # 将list转为map
+            paddle.dataset.movielens.max_user_id() + 1))
-    ret_val = dict()
+usr_emb = paddle.layer.embedding(input=uid, size=32)
-    for each in lst:
-        k, v = each
+usr_gender_id = paddle.layer.data(
-        ret_val[k] = v
+        name='gender_id', type=paddle.data_type.integer_value(2))
-    return ret_val
+usr_gender_emb = paddle.layer.embedding(input=usr_gender_id, size=16)
-def hook(settings, meta, **kwargs): # 读取meta.bin
+usr_age_id = paddle.layer.data(
-    # 定义电影特征
+        name='age_id',
-    movie_headers = list(meta_to_header(meta, 'movie'))
+        type=paddle.data_type.integer_value(
-    settings.movie_names = [h[0] for h in movie_headers]
+            len(paddle.dataset.movielens.age_table)))
-    headers = movie_headers
+usr_age_emb = paddle.layer.embedding(input=usr_age_id, size=16)
-    # 定义用户特征
+usr_job_id = paddle.layer.data(
-    user_headers = list(meta_to_header(meta, 'user'))
+        name='job_id',
-    settings.user_names = [h[0] for h in user_headers]
+        type=paddle.data_type.integer_value(paddle.dataset.movielens.max_job_id(
-    headers.extend(user_headers)
+        ) + 1))
+usr_job_emb = paddle.layer.embedding(input=usr_job_id, size=16)
-    # 加载评分信息
+```
-    headers.append(("rating", dense_vector(1)))
-    settings.input_types = __list_to_map__(headers)
-    settings.meta = meta
-@provider(init_hook=hook, cache=CacheType.CACHE_PASS_IN_MEM)
-def process(settings, filename):
-    with open(filename, 'r') as f:
-        for line in f:
-            # 从评分文件中读取评分
-            user_id, movie_id, score = map(int, line.split('::')[:-1])
-            # 将评分平移到[-2, +2]范围内的整数
-            score = float(score - 3)
-            movie_meta = settings.meta['movie'][movie_id]
-            user_meta = settings.meta['user'][user_id]
-            # 添加电影ID与电影特征
+如上述代码所示，对于每个用户，我们输入4维特征。其中包括`user_id`,`gender_id`,`age_id`,`job_id`。这几维特征均是简单的整数值。为了后续神经网络处理这些特征方便，我们借鉴NLP中的语言模型，将这几维离散的整数值，变换成embedding取出。分别形成`usr_emb`, `usr_gender_emb`, `usr_age_emb`, `usr_job_emb`。
-            outputs = [('movie_id', movie_id - 1)]
-            for i, each_meta in enumerate(movie_meta):
-                outputs.append((settings.movie_names[i + 1], each_meta))
+```python
+usr_combined_features = paddle.layer.fc(
-            # 添加用户ID与用户特征
+        input=[usr_emb, usr_gender_emb, usr_age_emb, usr_job_emb],
-            outputs.append(('user_id', user_id - 1))
+        size=200,
-            for i, each_meta in enumerate(user_meta):
+        act=paddle.activation.Tanh())
-                outputs.append((settings.user_names[i + 1], each_meta))
-            # 添加评分
-            outputs.append(('rating', [score]))
-            # 将数据返回给 paddle
-            yield __list_to_map__(outputs)
 ```
-## 模型配置说明
+然后，我们对于所有的用户特征，均输入到一个全连接层(fc)中。将所有特征融合为一个200维度的特征。
-### 数据定义
+进而，我们对每一个电影特征做类似的变换，网络配置为:
-加载`meta.bin`文件并定义通过`define_py_data_sources2`从dataprovider中读入数据：
 ```python
-from paddle.trainer_config_helpers import *
+mov_id = paddle.layer.data(
+    name='movie_id',
-try:
+    type=paddle.data_type.integer_value(
-    import cPickle as pickle
+        paddle.dataset.movielens.max_movie_id() + 1))
-except ImportError:
+mov_emb = paddle.layer.embedding(input=mov_id, size=32)
-    import pickle
+mov_categories = paddle.layer.data(
+    name='category_id',
+    type=paddle.data_type.sparse_binary_vector(
+        len(paddle.dataset.movielens.movie_categories())))
+mov_categories_hidden = paddle.layer.fc(input=mov_categories, size=32)
+movie_title_dict = paddle.dataset.movielens.get_movie_title_dict()
+mov_title_id = paddle.layer.data(
+    name='movie_title',
+    type=paddle.data_type.integer_value_sequence(len(movie_title_dict)))
+mov_title_emb = paddle.layer.embedding(input=mov_title_id, size=32)
+mov_title_conv = paddle.networks.sequence_conv_pool(
+    input=mov_title_emb, hidden_size=32, context_len=3)
+mov_combined_features = paddle.layer.fc(
+    input=[mov_emb, mov_categories_hidden, mov_title_conv],
+    size=200,
+    act=paddle.activation.Tanh())
+```
-is_predict = get_config_arg('is_predict', bool, False)
+电影ID和电影类型分别映射到其对应的特征隐层。对于电影标题名称(title)，一个ID序列表示的词语序列，在输入卷积层后，将得到每个时间窗口的特征（序列特征），然后通过在时间维度降采样得到固定维度的特征，整个过程在text_conv_pool实现。
-META_FILE = 'data/meta.bin'
+最后再将电影的特征融合进`mov_combined_features`中。
-# 加载 meta 文件
-with open(META_FILE, 'rb') as f:
-    meta = pickle.load(f)
-if not is_predict:
+```python
-    define_py_data_sources2(
+inference = paddle.layer.cos_sim(a=usr_combined_features, b=mov_combined_features, size=1, scale=5)
-        'data/train.list',
-        'data/test.list',
-        module='dataprovider',
-        obj='process',
-        args={'meta': meta})
 ```
-### 算法配置
+进而，我们使用余弦相似度计算用户特征与电影特征的相似性。并将这个相似性拟合(回归)到用户评分上。
-这里我们设置了batch size、网络初始学习率和RMSProp自适应优化方法。
 ```python
-settings(
+cost = paddle.layer.regression_cost(
-    batch_size=1600, learning_rate=1e-3, learning_method=RMSPropOptimizer())
+        input=inference,
+        label=paddle.layer.data(
+            name='score', type=paddle.data_type.dense_vector(1)))
 ```
-### 模型结构
+至此，我们的优化目标就是这个网络配置中的`cost`了。
-1. 定义数据输入和参数维度。
+## 训练模型
+### 定义参数
+神经网络的模型，我们可以简单的理解为网络拓朴结构+参数。之前一节，我们定义出了优化目标`cost`。这个`cost`即为网络模型的拓扑结构。我们开始训练模型，需要先定义出参数。定义方法为:
-   ```python
-   movie_meta = meta['movie']['__meta__']['raw_meta']
-   user_meta = meta['user']['__meta__']['raw_meta']
-   movie_id = data_layer('movie_id', size=movie_meta[0]['max'])    # 电影ID
+```python
-   title = data_layer('title', size=len(movie_meta[1]['dict']))    # 电影名称
+parameters = paddle.parameters.create(cost)
-   genres = data_layer('genres', size=len(movie_meta[2]['dict']))  # 电影类型
+```
-   user_id = data_layer('user_id', size=user_meta[0]['max'])	    # 用户ID
-   gender = data_layer('gender', size=len(user_meta[1]['dict']))   # 用户性别
-   age = data_layer('age', size=len(user_meta[2]['dict']))			# 用户年龄
-   occupation = data_layer('occupation', size=len(user_meta[3]['dict'])) # 用户职业
-   embsize = 256  # 向量维度
+    [INFO 2017-03-06 17:12:13,284 networks.py:1472] The input order is [user_id, gender_id, age_id, job_id, movie_id, category_id, movie_title, score]
-   ```
+    [INFO 2017-03-06 17:12:13,287 networks.py:1478] The output order is [__regression_cost_0__]
-2. 构造“电影”特征。
-   ```python
+`parameters`是模型的所有参数集合。他是一个python的dict。我们可以查看到这个网络中的所有参数名称。因为之前定义模型的时候，我们没有指定参数名称，这里参数名称是自动生成的。当然，我们也可以指定每一个参数名称，方便日后维护。
-   # 电影ID和电影类型分别映射到其对应的特征隐层（256维）。
-   movie_id_emb = embedding_layer(input=movie_id, size=embsize)
-   movie_id_hidden = fc_layer(input=movie_id_emb, size=embsize)
+```python
+print parameters.keys()
+```
-   genres_emb = fc_layer(input=genres, size=embsize)
+    [u'___fc_layer_2__.wbias', u'___fc_layer_2__.w2', u'___embedding_layer_3__.w0', u'___embedding_layer_5__.w0', u'___embedding_layer_2__.w0', u'___embedding_layer_1__.w0', u'___fc_layer_1__.wbias', u'___fc_layer_0__.wbias', u'___fc_layer_1__.w0', u'___fc_layer_0__.w2', u'___fc_layer_0__.w3', u'___fc_layer_0__.w0', u'___fc_layer_0__.w1', u'___fc_layer_2__.w1', u'___fc_layer_2__.w0', u'___embedding_layer_4__.w0', u'___sequence_conv_pool_0___conv_fc.w0', u'___embedding_layer_0__.w0', u'___sequence_conv_pool_0___conv_fc.wbias']
-   # 对于电影名称，一个ID序列表示的词语序列，在输入卷积层后，
-   # 将得到每个时间窗口的特征（序列特征），然后通过在时间维度
-   # 降采样得到固定维度的特征，整个过程在text_conv_pool实现
-   title_emb = embedding_layer(input=title, size=embsize)
-   title_hidden = text_conv_pool(
-       input=title_emb, context_len=5, hidden_size=embsize)
-   # 将三个属性的特征表示分别全连接并相加，结果即是电影特征的最终表示
+### 构造训练(trainer)
-   movie_feature = fc_layer(
-       input=[movie_id_hidden, title_hidden, genres_emb], size=embsize)
-   ```
-3. 构造“用户”特征。
+下面，我们根据网络拓扑结构和模型参数来构造出一个本地训练(trainer)。在构造本地训练的时候，我们还需要指定这个训练的优化方法。这里我们使用Adam来作为优化算法。
-   ```python
-   # 将用户ID，性别，职业，年龄四个属性分别映射到其特征隐层。
-   user_id_emb = embedding_layer(input=user_id, size=embsize)
-   user_id_hidden = fc_layer(input=user_id_emb, size=embsize)
-   gender_emb = embedding_layer(input=gender, size=embsize)
+```python
-   gender_hidden = fc_layer(input=gender_emb, size=embsize)
+trainer = paddle.trainer.SGD(cost=cost, parameters=parameters, 
+                            update_equation=paddle.optimizer.Adam(learning_rate=1e-4))
+```
-   age_emb = embedding_layer(input=age, size=embsize)
+    [INFO 2017-03-06 17:12:13,378 networks.py:1472] The input order is [user_id, gender_id, age_id, job_id, movie_id, category_id, movie_title, score]
-   age_hidden = fc_layer(input=age_emb, size=embsize)
+    [INFO 2017-03-06 17:12:13,379 networks.py:1478] The output order is [__regression_cost_0__]
-   occup_emb = embedding_layer(input=occupation, size=embsize)
-   occup_hidden = fc_layer(input=occup_emb, size=embsize)
-   # 同样将这四个属性分别全连接并相加形成用户特征的最终表示。
+### 训练
-   user_feature = fc_layer(
-       input=[user_id_hidden, gender_hidden, age_hidden, occup_hidden],
-       size=embsize)
-   ```
-4. 计算余弦相似度，定义损失函数和网络输出。
+下面我们开始训练过程。
-   ```python
+我们直接使用Paddle提供的数据集读取程序。`paddle.dataset.movielens.train()`和`paddle.dataset.movielens.test()`分别做训练和预测数据集。并且通过`reader_dict`来指定每一个数据和data_layer的对应关系。
-   similarity = cos_sim(a=movie_feature, b=user_feature, scale=2)
-   # 训练时，采用regression_cost作为损失函数计算回归误差代价，并作为网络的输出。
+例如，这里的reader_dict表示的是，对于数据层 `user_id`，使用了reader中每一条数据的第0个元素。`gender_id`数据层使用了第1个元素。以此类推。
-   # 预测时，网络的输出即为余弦相似度。
-   if not is_predict:
-       lbl=data_layer('rating', size=1)
-   	cost=regression_cost(input=similarity, label=lbl)
-   	outputs(cost)
-   else:
-       outputs(similarity)
-   ```
-## 训练模型
+训练过程是完全自动的。我们可以使用event_handler来观察训练过程，或进行测试等。这里我们在event_handler里面绘制了训练误差曲线和测试误差曲线。并且保存了模型。
-执行`sh train.sh` 开始训练模型，将日志写入文件 `log.txt` 并打印在屏幕上。其中指定了总共需要执行 50 个pass。
-```shell
-set -e
-paddle train \
-    --config=trainer_config.py \		 # 神经网络配置文件
-    --save_dir=./output \				 # 模型保存路径
-    --use_gpu=false \					 # 是否使用GPU(默认不使用)
-    --trainer_count=4\					 # 一台机器上面的线程数量
-    --test_all_data_in_one_period=true \ # 每个训练周期训练一次所有数据，否则每个训练周期测试batch_size个batch数据
-    --log_period=100 \					 # 训练log_period个batch后打印日志
-    --dot_period=1 \					 # 每训练dot_period个batch后打印一个"."
-    --num_passes=50  2>&1 | tee 'log.txt'
-```
-成功的输出类似如下：
+```python
+%matplotlib inline
-```bash
-I0117 01:01:48.585651  9998 TrainerInternal.cpp:165]  Batch=100 samples=160000 AvgCost=0.600042 CurrentCost=0.600042 Eval:  CurrentEval:
+import matplotlib.pyplot as plt
-...................................................................................................
+from IPython import display
-I0117 01:02:53.821918  9998 TrainerInternal.cpp:165]  Batch=200 samples=320000 AvgCost=0.602855 CurrentCost=0.605668 Eval:  CurrentEval:
+import cPickle
-...................................................................................................
-I0117 01:03:58.937922  9998 TrainerInternal.cpp:165]  Batch=300 samples=480000 AvgCost=0.605199 CurrentCost=0.609887 Eval:  CurrentEval:
+feeding = {
-...................................................................................................
+    'user_id': 0,
-I0117 01:05:04.083251  9998 TrainerInternal.cpp:165]  Batch=400 samples=640000 AvgCost=0.608693 CurrentCost=0.619175 Eval:  CurrentEval:
+    'gender_id': 1,
-...................................................................................................
+    'age_id': 2,
-I0117 01:06:09.155859  9998 TrainerInternal.cpp:165]  Batch=500 samples=800000 AvgCost=0.613273 CurrentCost=0.631591 Eval:  CurrentEval:
+    'job_id': 3,
-.................................................................I0117 01:06:51.109654  9998 TrainerInternal.cpp:181]
+    'movie_id': 4,
- Pass=49 Batch=565 samples=902826 AvgCost=0.614772 Eval:
+    'category_id': 5,
-I0117 01:07:04.205142  9998 Tester.cpp:115]  Test samples=97383 cost=0.721995 Eval:
+    'movie_title': 6,
-I0117 01:07:04.205281  9998 GradientMachine.cpp:113] Saving parameters to ./output/pass-00049
+    'score': 7
+}
+step=0
+train_costs=[],[]
+test_costs=[],[]
+def event_handler(event):
+    global step
+    global train_costs
+    global test_costs
+    if isinstance(event, paddle.event.EndIteration):
+        need_plot = False
+        if step % 10 == 0:  # every 10 batches, record a train cost
+            train_costs[0].append(step)
+            train_costs[1].append(event.cost)
+        if step % 1000 == 0: # every 1000 batches, record a test cost
+            result = trainer.test(reader=paddle.batch(
+                  paddle.dataset.movielens.test(), batch_size=256))
+            test_costs[0].append(step)
+            test_costs[1].append(result.cost)
+        if step % 100 == 0: # every 100 batches, update cost plot
+            plt.plot(*train_costs)
+            plt.plot(*test_costs)
+            plt.legend(['Train Cost', 'Test Cost'], loc='upper left')
+            display.clear_output(wait=True)
+            display.display(plt.gcf())
+            plt.gcf().clear()
+        step += 1
+trainer.train(
+    reader=paddle.batch(
+            paddle.reader.shuffle(
+            paddle.dataset.movielens.train(), buf_size=8192),
+                            batch_size=256),
+    event_handler=event_handler,
+    feeding=feeding,
+    num_passes=2)
 ```
+![png](image/output_31_0.png)
 ## 应用模型
-在训练了几轮以后，您可以对模型进行评估。运行以下命令，可以通过选择最小训练误差的一轮参数得到最好轮次的模型。
+在训练了几轮以后，您可以对模型进行推断。我们可以使用任意一个用户ID和电影ID，来预测该用户对该电影的评分。示例程序为:
-```shell
-./evaluate.py log.txt
-```
-您将看到：
+```python
+import copy
+user_id = 234
+movie_id = 345
-```shell
+user = user_info[user_id]
-Best pass is 00036, error is 0.719281, which means predict get error as 0.424052
+movie = movie_info[movie_id]
-evaluating from pass output/pass-00036
-```
+feature = user.value() + movie.value()
-预测任何用户对于任何一部电影评价的命令如下：
+infer_dict = copy.copy(feeding)
+del infer_dict['score']
-```shell
+prediction = paddle.infer(output=inference, parameters=parameters, input=[feature], feeding=infer_dict)
-python prediction.py 'output/pass-00036/'
+score = (prediction[0][0] + 5.0) / 2
+print "[Predict] User %d Rating Movie %d With Score %.2f"%(user_id, movie_id, score)
 ```
-预测程序将读取用户的输入，然后输出预测分数。您会看到如下命令行界面：
+    [INFO 2017-03-06 17:17:08,132 networks.py:1472] The input order is [user_id, gender_id, age_id, job_id, movie_id, category_id, movie_title]
+    [INFO 2017-03-06 17:17:08,134 networks.py:1478] The output order is [__cos_sim_0__]
+    [Predict] User 234 Rating Movie 345 With Score 4.16
-```
-Input movie_id: 1962
-Input user_id: 1
-Prediction Score is 4.25
-```
 ## 总结
@@ -380,3 +432,4 @@ Prediction Score is 4.25
 <br/>
 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">知识共享 署名-非商业性使用-相同方式共享 4.0 国际 许可协议</a>进行许可。
--- a/recommender_system/image/output_32_0.png
+++ b/recommender_system/image/output_32_0.png