diff --git a/README.md b/README.md
index 202d516dcfcfcb17a320cabd3428cdeec0da6e52..3a2d2f8da6ab8297600f21ebfef509f56df96017 100644
--- a/README.md
+++ b/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
diff --git a/README_CN.md b/README_CN.md
index 3a6a63e700bd205c2ea6b15a0232f6deb8a42bef..aadf944954634202c80160e3af1c8f5cdd18b284 100644
--- a/README_CN.md
+++ b/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) |
diff --git a/doc/imgs/flen.png b/doc/imgs/flen.png
new file mode 100644
index 0000000000000000000000000000000000000000..b8f6cbbe5833237b7a54c60801a142182970fa9b
Binary files /dev/null and b/doc/imgs/flen.png differ
diff --git a/models/rank/readme.md b/models/rank/readme.md
index 2d2ba2522d303624ae6ba81d799367fe99d8486a..da242481badbc4cb92a4b75dc277d3981f12f1fc 100644
--- a/models/rank/readme.md
+++ b/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 @@
+[FLEN](https://arxiv.org/pdf/1911.04690.pdf):
+
+
+
+
## 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