fix bug

README.md

@@ -59,7 +59,8 @@
     |   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   |                [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   |                    [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) |
     |  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

@@ -37,35 +37,43 @@
 | 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) |
 | 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) |
 
 下面是每个模型的简介（注：图片引用自链接中的论文）
 
 [wide&deep](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454):
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 <p align="center">
 <img align="center" src="../../doc/imgs/wide&deep.png">
 <p>
-
 [DeepFM](https://arxiv.org/pdf/1703.04247.pdf):
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 <p align="center">
 <img align="center" src="../../doc/imgs/deepfm.png">
 <p>
-
 [XDeepFM](https://dl.acm.org/doi/pdf/10.1145/3219819.3220023):
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 <p align="center">
 <img align="center" src="../../doc/imgs/xdeepfm.png">
 <p>
-
 [DCN](https://dl.acm.org/doi/pdf/10.1145/3124749.3124754):
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 <p align="center">
 <img align="center" src="../../doc/imgs/dcn.png">
 <p>
-
 [DIN](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823):
+
 <p align="center">
 <img align="center" src="../../doc/imgs/din.png">
 <p>
+[FIBINET](https://arxiv.org/pdf/1905.09433.pdf):
+
+<p align="center">
+<img align="center" src="../../doc/imgs/fibinet.png">
+<p>
 
 ## 使用教程(快速开始)
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 使用样例数据快速开始，参考[训练](###训练) & [预测](###预测)
 ## 使用教程(复现论文)
 为了方便使用者能够快速的跑通每一个模型，我们在每个模型下都提供了样例数据，并且调整了batch_size等超参以便在样例数据上更加友好的显示训练&测试日志。如果需要复现readme中的效果请按照如下表格调整batch_size等超参，并使用提供的脚本下载对应数据集以及数据预处理。