edit README.md

README.md

@@ -56,10 +56,10 @@
     |         Rank          |                  [xDeepFM](models/rank/xdeepfm/model.py)                  |   ✓   |    x    |        ✓         |     x     | [KDD 2018][xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3219819.3220023)                                                       |
     |         Rank          |                      [DIN](models/rank/din/model.py)                      |   ✓   |    x    |        ✓         |     x     | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823)                                                                                     |
     |   Rank   |                      [DIEN](models/rank/dien/model.py)                      |    ✓    |    x    |     ✓     |     x     | [AAAI 2019][Deep Interest Evolution Network for Click-Through Rate Prediction](https://www.aaai.org/ojs/index.php/AAAI/article/view/4545/4423)                                                                                     |
-    |   Rank   |                      [AutoInt](models/rank/AutoInt/model.py)                      |    ✓    |    x    |     ✓     |     x     | [CIKM 2019][AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/pdf/1810.11921.pdf)                                                                                     |
     |         Rank          |                [Wide&Deep](models/rank/wide_deep/model.py)                |   ✓   |    x    |        ✓         |     x     | [DLRS 2016][Wide & Deep Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454)                                                                                               |
     |         Rank          |                    [FGCNN](models/rank/fgcnn/model.py)                    |   ✓   |    ✓    |        ✓         |     ✓     | [WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf)                                                                      |
     |         Rank          |                  [Fibinet](models/rank/fibinet/model.py)                  |   ✓   |    ✓    |        ✓         |     ✓     | [RecSys19][FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction]( https://arxiv.org/pdf/1905.09433.pdf)                                                 |
+    |         Rank          |                  [Flen](models/rank/flen/model.py)                  |   ✓   |    ✓    |        ✓         |     ✓     | [2019][FLEN: Leveraging Field for Scalable CTR Prediction]( https://arxiv.org/pdf/1911.04690.pdf)                                                 |
     |      Multi-Task       |                  [ESMM](models/multitask/esmm/model.py)                   |   ✓   |    ✓    |        ✓         |     ✓     | [SIGIR 2018][Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://arxiv.org/abs/1804.07931)                                                              |
     |      Multi-Task       |                  [MMOE](models/multitask/mmoe/model.py)                   |   ✓   |    ✓    |        ✓         |     ✓     | [KDD 2018][Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts](https://dl.acm.org/doi/abs/10.1145/3219819.3220007)                                                       |
     |      Multi-Task       |           [ShareBottom](models/multitask/share-bottom/model.py)           |   ✓   |    ✓    |        ✓         |     ✓     | [1998][Multitask learning](http://reports-archive.adm.cs.cmu.edu/anon/1997/CMU-CS-97-203.pdf)                                                                                                               |
@@ -90,7 +90,7 @@
   > - Other installation problems can be raised in [Paddle Issue](https://github.com/PaddlePaddle/Paddle/issues) or [PaddleRec Issue](https://github.com/PaddlePaddle/PaddleRec/issues)
 
 2. **Install by source code**
-  
+
   - Install PaddlePaddle  
 
     ```shell

README_CN.md

@@ -61,10 +61,10 @@
     |   排序   |                  [xDeepFM](models/rank/xdeepfm/model.py)                  |    ✓    |    x    |     ✓     |     x     | [KDD 2018][xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3219819.3220023)                                                       |
     |   排序   |                      [DIN](models/rank/din/model.py)                      |    ✓    |    x    |     ✓     |     x     | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823)                                                                                     |
     |   排序   |                      [DIEN](models/rank/dien/model.py)                      |    ✓    |    x    |     ✓     |     x     | [AAAI 2019][Deep Interest Evolution Network for Click-Through Rate Prediction](https://www.aaai.org/ojs/index.php/AAAI/article/view/4545/4423)                                                                                     |
-    |   排序   |                      [AutoInt](models/rank/AutoInt/model.py)                      |    ✓    |    x    |     ✓     |     x     | [CIKM 2019][AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/pdf/1810.11921.pdf)                                                                                     |
     |   排序   |                [Wide&Deep](models/rank/wide_deep/model.py)                |    ✓    |    x    |     ✓     |     x     | [DLRS 2016][Wide & Deep Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454)                                                                                               |
     |   排序   |                    [FGCNN](models/rank/fgcnn/model.py)                    |    ✓    |    ✓    |     ✓     |     ✓     | [WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf)                                                                      |
     |   排序   |                  [Fibinet](models/rank/fibinet/model.py)                  |    ✓    |    ✓    |     ✓     |     ✓     | [RecSys19][FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction]( https://arxiv.org/pdf/1905.09433.pdf)                                                 |
+    |   排序   |                  [Flen](models/rank/flen/model.py)                  |    ✓    |    ✓    |     ✓     |     ✓     | [2019][FLEN: Leveraging Field for Scalable CTR Prediction]( https://arxiv.org/pdf/1911.04690.pdf)                                                 |
     |  多任务  |                  [ESMM](models/multitask/esmm/model.py)                   |    ✓    |    ✓    |     ✓     |     ✓     | [SIGIR 2018][Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://arxiv.org/abs/1804.07931)                                                              |
     |  多任务  |                  [MMOE](models/multitask/mmoe/model.py)                   |    ✓    |    ✓    |     ✓     |     ✓     | [KDD 2018][Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts](https://dl.acm.org/doi/abs/10.1145/3219819.3220007)                                                       |
     |  多任务  |           [ShareBottom](models/multitask/share-bottom/model.py)           |    ✓    |    ✓    |     ✓     |     ✓     | [1998][Multitask learning](http://reports-archive.adm.cs.cmu.edu/anon/1997/CMU-CS-97-203.pdf)                                                                                                               |

models/rank/readme.md

@@ -37,9 +37,9 @@
 | xDeepFM | xDeepFM | [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3219819.3220023)(2018) |
 | DIN | Deep Interest Network | [Deep Interest Network for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823)(2018) |
 | DIEN | Deep Interest Evolution Network | [Deep Interest Evolution Network for Click-Through Rate Prediction](https://www.aaai.org/ojs/index.php/AAAI/article/view/4545/4423)(2019) |
-| AutoInt | Automatic Feature Interaction Learning via Self-Attentive Neural Networks | [AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/pdf/1810.11921.pdf)(2019) |
 | FGCNN | Feature Generation by CNN | [Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf)(2019) |
 | FIBINET | Combining Feature Importance and Bilinear feature Interaction | [《FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction》]( https://arxiv.org/pdf/1905.09433.pdf)(2019) |
+| FLEN | Leveraging Field for Scalable CTR Prediction | [《FLEN: Leveraging Field for Scalable CTR Prediction》]( https://arxiv.org/pdf/1911.04690.pdf)（2019） |
 
 下面是每个模型的简介（注：图片引用自链接中的论文）
 
@@ -73,6 +73,11 @@
 <p align="center">
 <img align="center" src="../../doc/imgs/fibinet.png">
 <p>
+[FLEN](https://arxiv.org/pdf/1911.04690.pdf):
+
+<p align="center">
+<img align="center" src="../../doc/imgs/flen.png">
+<p>
 
 ## 使用教程(快速开始)
 
@@ -88,6 +93,7 @@
 |       Wide&Deep  |       40       |       1       |        40       |
 |       xDeepFM        |       100       |       1       |        10       |
 | Fibinet | 1000 | 8 | 4 |
+| Flen | 512 | 8 | 1 |
 ### 数据处理
 参考每个模型目录数据下载&预处理脚本
 
@@ -128,6 +134,7 @@ python -m paddlerec.run -m ./config.yaml # 以DNN为例
 |       Census-income Data        |       Wide&Deep       |       0.76195         |       0.90577          |       --          |       --          |
 |       Amazon Product        |       DIN       |       0.47005        |       0.86379         |       --          |       --          |
 | Criteo | Fibinet | -- | 0.86662 | -- | -- |
+| Avazu | Flen | -- | -- | -- | -- |
 
 
 ## 分布式