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‎README_CN.md‎

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| 排序 | [Wide&Deep](models/rank/wide_deep/)([文档](https://paddlerec.readthedocs.io/en/latest/models/rank/wide_deep.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238421) | ✓ | x | >=2.1.0 | [DLRS 2016][Wide & Deep Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454) |
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| 排序 | [FGCNN](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/fgcnn/) | - | ✓ | ✓ | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf) |
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| 排序 | [Fibinet](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/fibinet/) | - | ✓ | ✓ | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [RecSys19][FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction]( https://arxiv.org/pdf/1905.09433.pdf) |
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| 排序 | [Flen](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/flen/) | - | ✓ | ✓ | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [2019][FLEN: Leveraging Field for Scalable CTR Prediction]( https://arxiv.org/pdf/1911.04690.pdf) |
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| 排序 | [FLEN](models/rank/flen/) | - | ✓ | ✓ | >=2.1.0 | [2019][FLEN: Leveraging Field for Scalable CTR Prediction]( https://arxiv.org/pdf/1911.04690.pdf) |
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| 排序 | [DeepRec](models/rank/deeprec/) | - | ✓ | ✓ | >=2.1.0 | [2017][Training Deep AutoEncoders for Collaborative Filtering](https://arxiv.org/pdf/1708.01715v3.pdf) |
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| 排序 | [AutoFIS](models/rank/autofis/) | - | ✓ | ✓ | >=2.1.0 | [KDD 2020][AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction](https://arxiv.org/pdf/2003.11235v3.pdf) | | - | ✓ | ✓ | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [2019][FLEN: Leveraging Field for Scalable CTR Prediction]( https://arxiv.org/pdf/1911.04690.pdf) |
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| 排序 | [AutoFIS](models/rank/autofis/) | - | ✓ | ✓ | >=2.1.0 | [KDD 2020][AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction](https://arxiv.org/pdf/2003.11235v3.pdf)
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| 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) |
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| 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) |
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| 多任务 | [MMOE](models/multitask/mmoe/)([文档](https://paddlerec.readthedocs.io/en/latest/models/multitask/mmoe.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238934) | ✓ | ✓ | >=2.1.0 | [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) |

‎README_EN.md‎

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| Rank | [Wide&Deep](models/rank/wide_deep/)<br>([doc](https://paddlerec.readthedocs.io/en/latest/models/rank/wide_deep.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238421) | ✓ | x | >=2.1.0 | [DLRS 2016][Wide & Deep Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454) |
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| Rank | [FGCNN](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/fgcnn/) | - | ✓ | ✓ | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf) |
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| Rank | [Fibinet](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/fibinet/) | - | ✓ | ✓ | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [RecSys19][FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction]( https://arxiv.org/pdf/1905.09433.pdf) |
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| Rank | [Flen](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/flen/) | - | ✓ | ✓ | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [2019][FLEN: Leveraging Field for Scalable CTR Prediction]( https://arxiv.org/pdf/1911.04690.pdf) |
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| Rank | [FLEN](models/rank/flen/) | - | ✓ | ✓ | >=2.1.0 | [2019][FLEN: Leveraging Field for Scalable CTR Prediction]( https://arxiv.org/pdf/1911.04690.pdf) |
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| Rank | [DeepRec](models/rank/deeprec/) | - | ✓ | ✓ | >=2.1.0 | [2017][Training Deep AutoEncoders for Collaborative Filtering](https://arxiv.org/pdf/1708.01715v3.pdf) |
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| Rank | [AutoFIS](models/rank/autofis/) | - | ✓ | ✓ | >=2.1.0 | [KDD 2020][AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction](https://arxiv.org/pdf/2003.11235v3.pdf) |
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| 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) |

‎contributor.md‎

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| [AutoFIS](models/rank/autofis/) | [renmada](https://github.com/renmada) | https://github.com/PaddlePaddle/PaddleRec/pull/660 | 论文复现赛第五期 |
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| [Dselect_K](models/multitask/dselect_k/) | [Andy1314Chen](https://github.com/Andy1314Chen) | https://github.com/PaddlePaddle/PaddleRec/pull/671 | 论文复现赛第五期 |
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| [MIND](models/recall/mind/) | [duyiqi17 ](https://github.com/duyiqi17) | https://github.com/PaddlePaddle/PaddleRec/pull/398 | 其他 |
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| [FLEN](models/rank/flen/) | [LinJayan](https://github.com/LinJayan) | https://github.com/PaddlePaddle/PaddleRec/pull/685 | 论文复现赛第五期 |
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| [MHCN](models/recall/mhcn/) | [Andy1314Chen](https://github.com/Andy1314Chen) | https://github.com/PaddlePaddle/PaddleRec/pull/679 | 论文复现赛第五期 |
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