From a15f7df153d3e0d5d3f0b36e42c65478aa7eb7fa Mon Sep 17 00:00:00 2001 From: overlordmax <515704170@qq.com> Date: Mon, 22 Jun 2020 21:27:36 +0800 Subject: [PATCH] fix bug --- README.md | 3 ++- doc/imgs/fibinet.png | Bin 0 -> 81683 bytes models/rank/readme.md | 16 ++++++++++++---- 3 files changed, 14 insertions(+), 5 deletions(-) create mode 100644 doc/imgs/fibinet.png mode change 100755 => 100644 models/rank/readme.md diff --git a/README.md b/README.md index 9c826a09..eeec2145 100644 --- a/README.md +++ b/README.md @@ -59,7 +59,8 @@ | 排序 | [xDeepFM](models/rank/xdeepfm/model.py) | ✓ | x | ✓ | x | [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/model.py) | ✓ | x | ✓ | x | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823) | | 排序 | [Wide&Deep](models/rank/wide_deep/model.py) | ✓ | x | ✓ | x | [DLRS 2016][Wide & Deep Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454) | - | 排序 | [FGCNN](models/rank/fgcnn/model.py) | ✓ | ✓ | ✓ | ✓ | [WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf) | + | 排序 | [FGCNN](models/rank/fgcnn/model.py) | ✓ | ✓ | ✓ | ✓ | [WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf) + | 排序 | [Fibinet](models/rank/fibinet/model.py) | ✓ | ✓ | ✓ | ✓ | [RecSys19][FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction]( https://arxiv.org/pdf/1905.09433.pdf) | | 多任务 | [ESMM](models/multitask/esmm/model.py) | ✓ | ✓ | ✓ | ✓ | [SIGIR 2018][Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://arxiv.org/abs/1804.07931) | | 多任务 | [MMOE](models/multitask/mmoe/model.py) | ✓ | ✓ | ✓ | ✓ | [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) | | 多任务 | [ShareBottom](models/multitask/share-bottom/model.py) | ✓ | ✓ | ✓ | ✓ | [1998][Multitask learning](http://reports-archive.adm.cs.cmu.edu/anon/1997/CMU-CS-97-203.pdf) | diff --git a/doc/imgs/fibinet.png b/doc/imgs/fibinet.png new file mode 100644 index 0000000000000000000000000000000000000000..71d9b6c14219cdbf55b45d206206d11da6dc500a GIT binary patch literal 81683 zcmeFYbyQSe_&2J6ARyf>p>%^ZBRMqE4N}q)(kb1HNQ;Ot($Xp2Ez%uQGUPDi5CeBW zzwdp2?_KNucmKOTTo+nvImI)Po0=Ana@NN62$b7X^cN4<6ul-Tgo8 zb1t-c@ZdI2QC3RZ+jI|Mqi>>^`UkXJejd2vbFg?j?5=+9uAWg=Ry;gd{QabikG&;P z(FfWhhpzZuypw7*9O&&Fe*PE@CHPPcCG31ztVKwd8dtPkO0)Qz%7It3 z8LW4{((<;go##o{-QRHT`yR3TPfxly2=$x!|9#C6{QrM_XO{nuY%=UG?jIGI1ojr5 z4vu7u_ZBv0zIhQHbROkFq@{K@1;@u7Ygy*x@+7RY%!e*DBuOn^yki-0(Fx=#HF8)@ zIq(I7$mlWwm9wbJ2Y3cS!D&gh5$5E7HpA{7 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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) | +| FIBINET | Combining Feature Importance and Bilinear feature Interaction | [《FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction》]( https://arxiv.org/pdf/1905.09433.pdf) | 下面是每个模型的简介(注:图片引用自链接中的论文) [wide&deep](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454): +

- [DeepFM](https://arxiv.org/pdf/1703.04247.pdf): +

- [XDeepFM](https://dl.acm.org/doi/pdf/10.1145/3219819.3220023): +

- [DCN](https://dl.acm.org/doi/pdf/10.1145/3124749.3124754): +

- [DIN](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823): +

+[FIBINET](https://arxiv.org/pdf/1905.09433.pdf): + +

+ +

## 使用教程(快速开始) + 使用样例数据快速开始,参考[训练](###训练) & [预测](###预测) ## 使用教程(复现论文) 为了方便使用者能够快速的跑通每一个模型,我们在每个模型下都提供了样例数据,并且调整了batch_size等超参以便在样例数据上更加友好的显示训练&测试日志。如果需要复现readme中的效果请按照如下表格调整batch_size等超参,并使用提供的脚本下载对应数据集以及数据预处理。 -- GitLab