Merge branch 'master' into metaheac

PaddlePaddle · Jun 15, 2022 · a8db48b · a8db48b
2 parents d774f05 + d10fd27
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diff --git a/README_CN.md b/README_CN.md
@@ -172,6 +172,8 @@ python -u tools/static_trainer.py -m models/rank/dnn/config.yaml #  静态图训
   |   排序   |                                                                        [DCN_V2](models/rank/dcn_v2/)                                                                        |  -  |       ✓     |     ✓     | >=2.1.0 | [WWW 2021][DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://arxiv.org/pdf/2008.13535v2.pdf)|
   |   排序   |                  [DSIN](models/rank/dsin/)                                                                          |  -  |       ✓     |     ✓     | >=2.1.0 | [IJCAI 2019][Deep Session Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.06482v1.pdf)  |
   |   排序   |                     [SIGN](models/rank/sign/)([文档](https://paddl7erec.readthedocs.io/en/latest/models/rank/sign.html))                                                     |  [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3869111)  |       ✓     |     ✓     | >=2.1.0 | [AAAI 2021][Detecting Beneficial Feature Interactions for Recommender Systems](https://arxiv.org/pdf/2008.00404v6.pdf)                             |
+ |   排序   |                                    [IPRec](models/rank/iprec/)([文档](https://paddl7erec.readthedocs.io/en/latest/models/rank/iprec.html))                                    |                                      -                                      |       ✓     |     ✓     | >=2.1.0 | [SIGIR 2021][Package Recommendation with Intra- and Inter-Package Attention Networks](http://nlp.csai.tsinghua.edu.cn/~xrb/publications/SIGIR-21_IPRec.pdf)    |   多任务   |                                                                          [AITM](models/rank/aitm/)                                                                          |  -  |       ✓     |     ✓     | >=2.1.0 | [KDD 2021][Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising](https://arxiv.org/pdf/2105.08489v2.pdf)  |
+   |   排序   |                     [FGCNN](models/rank/fgcnn/)| - |       ✓     |     ✓     | >=2.1.0 | [WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf)                             |
   |   多任务   |                                                                          [AITM](models/rank/aitm/)                                                                          |  -  |       ✓     |     ✓     | >=2.1.0 | [KDD 2021][Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising](https://arxiv.org/pdf/2105.08489v2.pdf)  |
   |  多任务  |                                  [PLE](models/multitask/ple/)([文档](https://paddlerec.readthedocs.io/en/latest/models/multitask/ple.html))                                   |  [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238938)  |       ✓     |     ✓     |  >=2.1.0 | [RecSys 2020][Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations](https://dl.acm.org/doi/abs/10.1145/3383313.3412236)                                                              |
   |  多任务  |                                 [ESMM](models/multitask/esmm/)([文档](https://paddlerec.readthedocs.io/en/latest/models/multitask/esmm.html))                                 |  [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238583)  |       ✓     |     ✓     | >=2.1.0 | [SIGIR 2018][Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://arxiv.org/abs/1804.07931)                                                              |

diff --git a/README_EN.md b/README_EN.md
@@ -163,6 +163,8 @@ python -u tools/static_trainer.py -m models/rank/dnn/config.yaml #  Training wit
   |   Rank   |                     [DCN_V2](models/rank/dcn_v2/)                     |  -  |       ✓     |     ✓     | >=2.1.0 | [WWW 2021][DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://arxiv.org/pdf/2008.13535v2.pdf)|
   |   Rank   |                  [DSIN](models/rank/dsin/)                                                                          |  -  |       ✓     |     ✓     | >=2.1.0 | [IJCAI 2019][Deep Session Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.06482v1.pdf)  |
   |   Rank   |                     [SIGN](models/rank/sign/)([doc](https://paddlerec.readthedocs.io/en/latest/models/rank/sign.html))                     |  [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3869111)  |       ✓     |     ✓     | >=2.1.0 | [AAAI 2021][Detecting Beneficial Feature Interactions for Recommender Systems](https://arxiv.org/pdf/2008.00404v6.pdf) |
+  |   Rank   |                     [FGCNN](models/rank/fgcnn/)| - |       ✓     |     ✓     | >=2.1.0 | [WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf)                             |
+  |   Rank   |                                      [IPRec](models/rank/iprec/)([doc](https://paddl7erec.readthedocs.io/en/latest/models/rank/iprec.html))                                       |                                      -                                      |       ✓     |     ✓     | >=2.1.0 | [SIGIR 2021][Package Recommendation with Intra- and Inter-Package Attention Networks](http://nlp.csai.tsinghua.edu.cn/~xrb/publications/SIGIR-21_IPRec.pdf)                             |
   |   Multi-Task   |                                                                          [AITM](models/rank/aitm/)                                                                          |  -  |       ✓     |     ✓     | >=2.1.0 | [KDD 2021][Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising](https://arxiv.org/pdf/2105.08489v2.pdf)  |
   |      Multi-Task       |                  [PLE](models/multitask/ple/)<br>([doc](https://paddlerec.readthedocs.io/en/latest/models/multitask/ple.html))                   |  [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238938)  |     ✓     |     ✓     |  >=2.1.0 | [RecSys 2020][Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations](https://dl.acm.org/doi/abs/10.1145/3383313.3412236)                                                              |
   |      Multi-Task       |                  [ESMM](models/multitask/esmm/)<br>([doc](https://paddlerec.readthedocs.io/en/latest/models/multitask/esmm.html))                   |  [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238583)  |         ✓         |     ✓     |      >=2.1.0     | [SIGIR 2018][Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://arxiv.org/abs/1804.07931)                                                              |