add model

Author: yinhaofeng

README.md

@@ -131,6 +131,8 @@ python -u tools/static_trainer.py -m models/rank/dnn/config.yaml #  静态图训
   |   排序   |                  [xDeepFM](models/rank/xdeepfm/)                  |    ✓    |   ✓    |     ✓     |     x     | 2.1.0 | [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/)                      |    ✓    |    ✓    |     ✓     |     x     | 2.1.0 | [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/)                     |    ✓    |    ✓    |     ✓     |     x     | 2.1.0 | [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   |                     [dlrm](models/rank/dlrm/)                     |    ✓    |    ✓    |     ✓     |     x     | 2.1.0 | [CoRR 2019][Deep Learning Recommendation Model for Personalization and Recommendation Systems](https://arxiv.org/abs/1906.00091)                                                              |
+  |   排序   |                     [DeepFEFM](models/rank/deepfefm/)                     |    ✓    |    ✓    |     ✓     |     x     | 2.1.0 | [arXiv 2020][Field-Embedded Factorization Machines for Click-through rate prediction](https://arxiv.org/abs/2009.09931)                                                              |
   |   排序   |                      [BST](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/BST/)                      |    ✓    |    ✓    |     ✓     |     x     |  [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [DLP_KDD 2019][Behavior Sequence Transformer for E-commerce Recommendation in Alibaba](https://arxiv.org/pdf/1905.06874v1.pdf)                                                                              |
   |   排序   |                  [AutoInt](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/AutoInt/)                  |    ✓    |    ✓    |     ✓     |     x     |  [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [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/)                |    ✓    |    ✓    |     ✓     |     x     | 2.1.0 | [DLRS 2016][Wide & Deep Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454)                                                                                               |

README_EN.md

@@ -126,6 +126,8 @@ python -u tools/static_trainer.py -m models/rank/dnn/config.yaml #  Training wit
   |         Rank          |                  [xDeepFM](models/rank/xdeepfm/)                  |   ✓   |    ✓    |        ✓         |     x     | 2.1.0 | [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/)                      |   ✓   |    ✓    |        ✓         |     x     | 2.1.0 | [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/)                     |   ✓   |    ✓    |        ✓         |     x     | 2.1.0 | [AAAI 2019][Deep Interest Evolution Network for Click-Through Rate Prediction](https://www.aaai.org/ojs/index.php/AAAI/article/view/4545/4423)                                                              |
+  |         Rank          |                     [dlrm](models/rank/dlrm/)                     |   ✓   |    ✓    |        ✓         |     x     | 2.1.0 | [CoRR 2019][Deep Learning Recommendation Model for Personalization and Recommendation Systems](https://arxiv.org/abs/1906.00091)                                                              |
+  |         Rank          |                     [DeepFEFM](models/rank/deepfefm/)                     |   ✓   |    ✓    |        ✓         |     x     | 2.1.0 | [arXiv 2020][Field-Embedded Factorization Machines for Click-through rate prediction](https://arxiv.org/abs/2009.09931)                                                              |
   |         Rank          |                      [BST](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/BST/)                      |   ✓   |    ✓    |        ✓         |     x     | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [DLP-KDD 2019][Behavior Sequence Transformer for E-commerce Recommendation in Alibaba](https://arxiv.org/pdf/1905.06874v1.pdf)                                                                              |
   |         Rank          |                  [AutoInt](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/AutoInt/)                  |   ✓   |    ✓    |        ✓         |     x     | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [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/)                |   ✓   |    ✓    |        ✓         |     x     |      2.1.0     | [DLRS 2016][Wide & Deep Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454)                                                                                               |