From 99d931714fb461334bf01672ddcc150232a69ae1 Mon Sep 17 00:00:00 2001 From: yaoxuefeng Date: Thu, 11 Jun 2020 00:00:53 +0800 Subject: [PATCH] update readme with rank models --- README.md | 1 + models/rank/readme.md | 3 ++- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index b326d4c5..e72be600 100644 --- a/README.md +++ b/README.md @@ -56,6 +56,7 @@ | 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) | ✓ | ✓ | ✓ | diff --git a/models/rank/readme.md b/models/rank/readme.md index 0ac193c0..76892c99 100755 --- a/models/rank/readme.md +++ b/models/rank/readme.md @@ -1,7 +1,7 @@ # 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。 @@ -34,6 +34,7 @@ | 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） -- GitLab