未验证 提交 a05f027f 编写于 作者: T tangwei12 提交者: GitHub

Merge branch 'master' into fix_ci

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| 排序 | [xDeepFM](models/rank/xdeepfm/model.py) | ✓ | x | ✓ |
| 排序 | [DIN](models/rank/din/model.py) | ✓ | x | ✓ |
| 排序 | [Wide&Deep](models/rank/wide_deep/model.py) | ✓ | x | ✓ |
| 排序 | [FGCNN](models/rank/fgcnn/model.py) | ✓ | x | ✓ |
| 多任务 | [ESMM](models/multitask/esmm/model.py) | ✓ | ✓ | ✓ |
| 多任务 | [MMOE](models/multitask/mmoe/model.py) | ✓ | ✓ | ✓ |
| 多任务 | [ShareBottom](models/multitask/share-bottom/model.py) | ✓ | ✓ | ✓ |
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# 排序模型库
## 简介
我们提供了常见的排序任务中使用的模型算法的PaddleRec实现, 单机训练&预测效果指标以及分布式训练&预测性能指标等。实现的排序模型包括 [logistic regression](logistic_regression)[多层神经网络](dnn)[FM](fm)[FFM](ffm)[PNN](pnn)[多层神经网络](dnn)[Deep Cross Network](dcn)[DeepFM](deepfm)[xDeepFM](xdeepfm)[NFM](nfm)[AFM](afm)[Deep Interest Network](din)[Wide&Deep](wide_deep)
我们提供了常见的排序任务中使用的模型算法的PaddleRec实现, 单机训练&预测效果指标以及分布式训练&预测性能指标等。实现的排序模型包括 [logistic regression](logistic_regression)[多层神经网络](dnn)[FM](fm)[FFM](ffm)[PNN](pnn)[多层神经网络](dnn)[Deep Cross Network](dcn)[DeepFM](deepfm)[xDeepFM](xdeepfm)[NFM](nfm)[AFM](afm)[Deep Interest Network](din)[Wide&Deep](wide_deep)[FGCNN](fgcnn)
模型算法库在持续添加中,欢迎关注。
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| AFM | Attentional Factorization Machines | [Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](https://arxiv.org/pdf/1708.04617.pdf)(2017) |
| 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) |
下面是每个模型的简介(注:图片引用自链接中的论文)
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