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 ## 内容导航

 - **第一章 推荐系统基础**
-
  - **1.0 机器学习基础**
    - 1.0.1 机器学习基本流程
    - [1.0.2 逻辑回归](https://github.com/datawhalechina/fun-rec/blob/131bcee1f2d677041a4ec1c3f767c20d2d818780/docs/%E7%AC%AC%E4%B8%80%E7%AB%A0%20%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F%E5%9F%BA%E7%A1%80/1.0%20%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E7%A1%80/1.0.2%20%E9%80%BB%E8%BE%91%E5%9B%9E%E5%BD%92.md)
    - 1.0.3 神经网络
    - [1.0.4 常用优化算法](https://github.com/datawhalechina/fun-rec/blob/131bcee1f2/docs/%E7%AC%AC%E4%B8%80%E7%AB%A0%20%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F%E5%9F%BA%E7%A1%80/1.0%20%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E7%A1%80/1.0.4%20%E5%B8%B8%E7%94%A8%E4%BC%98%E5%8C%96%E7%AE%97%E6%B3%95.md)
    - [1.0.5 深度学习模型搭建基础](https://github.com/datawhalechina/fun-rec/blob/master/docs/%E7%AC%AC%E4%B8%80%E7%AB%A0%20%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F%E5%9F%BA%E7%A1%80/1.0%20%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E7%A1%80/1.0.5%20%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E6%A8%A1%E5%9E%8B%E6%90%AD%E5%BB%BA%E5%9F%BA%E7%A1%80.md)
-
+  
  - **1.1 基础推荐算法**
      - [1.1.1 推荐系统概述](https://github.com/datawhalechina/fun-rec/blob/master/docs/%E7%AC%AC%E4%B8%80%E7%AB%A0%20%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F%E5%9F%BA%E7%A1%80/1.1%E5%9F%BA%E7%A1%80%E6%8E%A8%E8%8D%90%E7%AE%97%E6%B3%95/1.1.1%20%E6%A6%82%E8%BF%B0.md)
      - [1.1.2 协同过滤](https://github.com/datawhalechina/fun-rec/blob/master/docs/%E7%AC%AC%E4%B8%80%E7%AB%A0%20%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F%E5%9F%BA%E7%A1%80/1.1%E5%9F%BA%E7%A1%80%E6%8E%A8%E8%8D%90%E7%AE%97%E6%B3%95/1.1.2%20%E5%8D%8F%E5%90%8C%E8%BF%87%E6%BB%A4.md)
      - [1.1.3 矩阵分解](https://github.com/datawhalechina/fun-rec/blob/master/docs/%E7%AC%AC%E4%B8%80%E7%AB%A0%20%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F%E5%9F%BA%E7%A1%80/1.1%E5%9F%BA%E7%A1%80%E6%8E%A8%E8%8D%90%E7%AE%97%E6%B3%95/1.1.3%20%E7%9F%A9%E9%98%B5%E5%88%86%E8%A7%A3.md)
      - [1.1.4 FM](https://github.com/datawhalechina/fun-rec/blob/master/docs/%E7%AC%AC%E4%B8%80%E7%AB%A0%20%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F%E5%9F%BA%E7%A1%80/1.1%E5%9F%BA%E7%A1%80%E6%8E%A8%E8%8D%90%E7%AE%97%E6%B3%95/1.1.4%20FM.md)
      - [1.1.5 GBDT+LR](https://github.com/datawhalechina/fun-rec/blob/master/docs/%E7%AC%AC%E4%B8%80%E7%AB%A0%20%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F%E5%9F%BA%E7%A1%80/1.1%E5%9F%BA%E7%A1%80%E6%8E%A8%E8%8D%90%E7%AE%97%E6%B3%95/1.1.6%20GBDT%2BLR.md)
-
+  
  - **1.2 深度推荐模型**
      - [1.2.1 NeuralCF](https://github.com/datawhalechina/fun-rec/blob/master/docs/%E7%AC%AC%E4%B8%80%E7%AB%A0%20%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F%E5%9F%BA%E7%A1%80/1.2%E5%9F%BA%E4%BA%8E%E6%B7%B1%E5%BA%A6%E7%BB%84%E5%90%88%E7%9A%84%E6%B7%B1%E5%BA%A6%E6%8E%A8%E8%8D%90%E6%A8%A1%E5%9E%8B/1.2.1%20NeuralCF.md)
      - [1.2.2 Deep Crossing](https://github.com/datawhalechina/fun-rec/blob/master/docs/%E7%AC%AC%E4%B8%80%E7%AB%A0%20%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F%E5%9F%BA%E7%A1%80/1.2%E5%9F%BA%E4%BA%8E%E6%B7%B1%E5%BA%A6%E7%BB%84%E5%90%88%E7%9A%84%E6%B7%B1%E5%BA%A6%E6%8E%A8%E8%8D%90%E6%A8%A1%E5%9E%8B/1.2.2%20DeepCrossing.md)
@@ -41,7 +40,7 @@
      - [1.2.10 DIEN](https://github.com/datawhalechina/fun-rec/blob/master/docs/%E7%AC%AC%E4%B8%80%E7%AB%A0%20%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F%E5%9F%BA%E7%A1%80/1.2%20%E6%B7%B1%E5%BA%A6%E6%8E%A8%E8%8D%90%E6%A8%A1%E5%9E%8B/1.2.10%20DIEN.md)
      -  [1.2.11 多任务学习](https://github.com/datawhalechina/fun-rec/blob/master/docs/%E7%AC%AC%E4%B8%80%E7%AB%A0%20%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F%E5%9F%BA%E7%A1%80/1.2%20%E6%B7%B1%E5%BA%A6%E6%8E%A8%E8%8D%90%E6%A8%A1%E5%9E%8B/1.2.11%20%E5%A4%9A%E4%BB%BB%E5%8A%A1%E5%AD%A6%E4%B9%A0.md)
      -  ......
-
+  
 - **第二章 推荐系统实战**
    - **2.1 竞赛实践(天池入门赛-新闻推荐)**
        - [2.1.0 入门赛讲解视频](https://www.bilibili.com/video/BV1do4y1d7FP?from=search&seid=17030616569563190806&spm_id_from=333.337.0.0)
@@ -50,8 +49,8 @@
        - [2.1.3 多路召回](https://github.com/datawhalechina/fun-rec/blob/master/docs/%E7%AC%AC%E4%BA%8C%E7%AB%A0%20%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F%E8%BF%9B%E9%98%B6/2.1%E7%AB%9E%E8%B5%9B%E5%AE%9E%E8%B7%B5/jupyter/2.3%20%E5%A4%9A%E8%B7%AF%E5%8F%AC%E5%9B%9E.ipynb)
        - [2.1.4 特征工程](https://github.com/datawhalechina/fun-rec/blob/master/docs/%E7%AC%AC%E4%BA%8C%E7%AB%A0%20%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F%E8%BF%9B%E9%98%B6/2.1%E7%AB%9E%E8%B5%9B%E5%AE%9E%E8%B7%B5/jupyter/2.4%20%E7%89%B9%E5%BE%81%E5%B7%A5%E7%A8%8B.ipynb)
        - [2.1.5 排序模型&模型融合](https://github.com/datawhalechina/fun-rec/blob/master/docs/%E7%AC%AC%E4%BA%8C%E7%AB%A0%20%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F%E8%BF%9B%E9%98%B6/2.1%E7%AB%9E%E8%B5%9B%E5%AE%9E%E8%B7%B5/jupyter/2.5%20%E6%8E%92%E5%BA%8F%E6%A8%A1%E5%9E%8B%2B%E6%A8%A1%E5%9E%8B%E8%9E%8D%E5%90%88.ipynb)
-        
    - **2.2 新闻推荐系统实践**
+      - **[2.2.0 新闻推荐系统流程的构建视频讲解](https://datawhale.feishu.cn/minutes/obcnzns778b725r5l535j32o)**
    
      - **2.2.1 离线物料系统的构建**
        - [2.2.1.1 Mysql基础](https://github.com/datawhalechina/fun-rec/blob/master/docs/%E7%AC%AC%E4%BA%8C%E7%AB%A0%20%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F%E5%AE%9E%E6%88%98/2.2%E6%96%B0%E9%97%BB%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F%E5%AE%9E%E6%88%98/docs/2.2.1.1%20Mysql%E5%9F%BA%E7%A1%80.md)
@@ -92,16 +91,23 @@



-**新闻推荐系统前端显示页面及后端系统架构（项目没有任何商用价值仅供入门者学习**）
-
-<div align=center> <img src="http://ryluo.oss-cn-chengdu.aliyuncs.com/图片image-20211205142026937.png" alt="image-20211205142026937" style="zoom:57%;" />
-</div>
+### 备注

+[2.1 竞赛实践(天池入门赛-新闻推荐)](https://tianchi.aliyun.com/competition/entrance/531842/forum)

 <div align=center>
-<img src="http://ryluo.oss-cn-chengdu.aliyuncs.com/图片Fun-Rec新闻推荐系统.png" alt="Fun-Rec新闻推荐系统" style="zoom:55%;" />
+    <img src="http://ryluo.oss-cn-chengdu.aliyuncs.com/图片image-20211213165802957.png" alt="image-20211213165802957" style="zoom:50%;" />
+    <img src="http://ryluo.oss-cn-chengdu.aliyuncs.com/图片image-20211213165847593.png" alt="image-20211213165847593" style="zoom:50%;" />
 </div>

+**2.2 新闻推荐系统实践前端展示和后端逻辑(项目没有任何商用价值仅供入门者学习)**
+
+<div align=center> 
+    <img src="http://ryluo.oss-cn-chengdu.aliyuncs.com/图片image-20211205142026937.png" alt="image-20211205142026937" style="zoom:57%;" />
+    <img src="http://ryluo.oss-cn-chengdu.aliyuncs.com/图片Fun-Rec新闻推荐系统.png" alt="Fun-Rec新闻推荐系统" style="zoom:50%;" />
+</div>
+
+


 ## 致谢