From 1416d8ee4f625b7c1902093e5e02c35be19bdcf0 Mon Sep 17 00:00:00 2001 From: chengmo Date: Mon, 29 Jun 2020 13:53:50 +0800 Subject: [PATCH] update --- README.md | 6 +- README_en.md | 191 +++++++++++++++++------------------ doc/imgs/overview_en.png | Bin 0 -> 471076 bytes doc/imgs/rec-overview-en.png | Bin 0 -> 467939 bytes 4 files changed, 98 insertions(+), 99 deletions(-) create mode 100644 doc/imgs/overview_en.png create mode 100644 doc/imgs/rec-overview-en.png diff --git a/README.md b/README.md index eeec2145..a66b15f3 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,5 @@ +(简体中文|[English](./README_en.md)) +

@@ -59,8 +61,8 @@ | 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 | [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) | + | 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/README_en.md b/README_en.md index 815a6edc..f0b62656 100644 --- a/README_en.md +++ b/README_en.md @@ -2,16 +2,13 @@

- -

-

- +

What is recommendation system ?

- +

- Recommendation system is the key to help users get information of interest efficiently in the era of explosive growth of Internet information @@ -27,79 +24,79 @@

What is PaddleRec ?

-- 源于飞桨生态的搜索推荐模型 **一站式开箱即用工具** -- 适合初学者,开发者,研究者的推荐系统全流程解决方案 -- 包含内容理解、匹配、召回、排序、 多任务、重排序等多个任务的完整推荐搜索算法库 - - - | 方向 | 模型 | 单机CPU | 单机GPU | 分布式CPU | 分布式GPU | 论文 | - | :------: | :-----------------------------------------------------------------------: | :-----: | :-----: | :-------: | :-------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | - | 内容理解 | [Text-Classifcation](models/contentunderstanding/classification/model.py) | ✓ | ✓ | ✓ | x | [EMNLP 2014][Convolutional neural networks for sentence classication](https://www.aclweb.org/anthology/D14-1181.pdf) | - | 内容理解 | [TagSpace](models/contentunderstanding/tagspace/model.py) | ✓ | ✓ | ✓ | x | [EMNLP 2014][TagSpace: Semantic Embeddings from Hashtags](https://www.aclweb.org/anthology/D14-1194.pdf) | - | 匹配 | [DSSM](models/match/dssm/model.py) | ✓ | ✓ | ✓ | x | [CIKM 2013][Learning Deep Structured Semantic Models for Web Search using Clickthrough Data](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/cikm2013_DSSM_fullversion.pdf) | - | 匹配 | [MultiView-Simnet](models/match/multiview-simnet/model.py) | ✓ | ✓ | ✓ | x | [WWW 2015][A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/frp1159-songA.pdf) | - | 召回 | [TDM](models/treebased/tdm/model.py) | ✓ | >=1.8.0 | ✓ | >=1.8.0 | [KDD 2018][Learning Tree-based Deep Model for Recommender Systems](https://arxiv.org/pdf/1801.02294.pdf) | - | 召回 | [fasttext](models/recall/fasttext/model.py) | ✓ | ✓ | x | x | [EACL 2017][Bag of Tricks for Efficient Text Classification](https://www.aclweb.org/anthology/E17-2068.pdf) | - | 召回 | [Word2Vec](models/recall/word2vec/model.py) | ✓ | ✓ | ✓ | x | [NIPS 2013][Distributed Representations of Words and Phrases and their Compositionality](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf) | - | 召回 | [SSR](models/recall/ssr/model.py) | ✓ | ✓ | ✓ | ✓ | [SIGIR 2016][Multi-Rate Deep Learning for Temporal Recommendation](http://sonyis.me/paperpdf/spr209-song_sigir16.pdf) | - | 召回 | [Gru4Rec](models/recall/gru4rec/model.py) | ✓ | ✓ | ✓ | ✓ | [2015][Session-based Recommendations with Recurrent Neural Networks](https://arxiv.org/abs/1511.06939) | - | 召回 | [Youtube_dnn](models/recall/youtube_dnn/model.py) | ✓ | ✓ | ✓ | ✓ | [RecSys 2016][Deep Neural Networks for YouTube Recommendations](https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45530.pdf) | - | 召回 | [NCF](models/recall/ncf/model.py) | ✓ | ✓ | ✓ | ✓ | [WWW 2017][Neural Collaborative Filtering](https://arxiv.org/pdf/1708.05031.pdf) | - | 召回 | [GNN](models/recall/gnn/model.py) | ✓ | ✓ | ✓ | ✓ | [AAAI 2019][Session-based Recommendation with Graph Neural Networks](https://arxiv.org/abs/1811.00855) | - | 排序 | [Logistic Regression](models/rank/logistic_regression/model.py) | ✓ | x | ✓ | x | / | - | 排序 | [Dnn](models/rank/dnn/model.py) | ✓ | ✓ | ✓ | ✓ | / | - | 排序 | [FM](models/rank/fm/model.py) | ✓ | x | ✓ | x | [IEEE Data Mining 2010][Factorization machines](https://analyticsconsultores.com.mx/wp-content/uploads/2019/03/Factorization-Machines-Steffen-Rendle-Osaka-University-2010.pdf) | - | 排序 | [FFM](models/rank/ffm/model.py) | ✓ | x | ✓ | x | [RECSYS 2016][Field-aware Factorization Machines for CTR Prediction](https://dl.acm.org/doi/pdf/10.1145/2959100.2959134) | - | 排序 | [FNN](models/rank/fnn/model.py) | ✓ | x | ✓ | x | [ECIR 2016][Deep Learning over Multi-field Categorical Data](https://arxiv.org/pdf/1601.02376.pdf) | - | 排序 | [Deep Crossing](models/rank/deep_crossing/model.py) | ✓ | x | ✓ | x | [ACM 2016][Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features](https://www.kdd.org/kdd2016/papers/files/adf0975-shanA.pdf) | - | 排序 | [Pnn](models/rank/pnn/model.py) | ✓ | x | ✓ | x | [ICDM 2016][Product-based Neural Networks for User Response Prediction](https://arxiv.org/pdf/1611.00144.pdf) | - | 排序 | [DCN](models/rank/dcn/model.py) | ✓ | x | ✓ | x | [KDD 2017][Deep & Cross Network for Ad Click Predictions](https://dl.acm.org/doi/pdf/10.1145/3124749.3124754) | - | 排序 | [NFM](models/rank/nfm/model.py) | ✓ | x | ✓ | x | [SIGIR 2017][Neural Factorization Machines for Sparse Predictive Analytics](https://dl.acm.org/doi/pdf/10.1145/3077136.3080777) | - | 排序 | [AFM](models/rank/afm/model.py) | ✓ | x | ✓ | x | [IJCAI 2017][Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](https://arxiv.org/pdf/1708.04617.pdf) | - | 排序 | [DeepFM](models/rank/deepfm/model.py) | ✓ | x | ✓ | x | [IJCAI 2017][DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://arxiv.org/pdf/1703.04247.pdf) | - | 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 | [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) | - | 重排序 | [Listwise](models/rerank/listwise/model.py) | ✓ | ✓ | ✓ | x | [2019][Sequential Evaluation and Generation Framework for Combinatorial Recommender System](https://arxiv.org/pdf/1902.00245.pdf) | - - - - - -

快速安装

- -### 环境要求 +- A quick start tool of search & recommendation model based on [PaddlePaddle](https://www.paddlepaddle.org.cn/documentation/docs/en/beginners_guide/index_en.html) +- The whole process solution of recommendation system for beginners, developers and researchers +- Complete recommendation algorithm library including content understanding, matching, recall, ranking, multi-task, re-rank etc. + + + | Type | Algorithm | CPU | GPU | Parameter-Server | Multi-GPU | Paper | + | :-------------------: | :-----------------------------------------------------------------------: | :---: | :-----: | :--------------: | :-------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | + | Content-Understanding | [Text-Classifcation](models/contentunderstanding/classification/model.py) | ✓ | ✓ | ✓ | x | [EMNLP 2014][Convolutional neural networks for sentence classication](https://www.aclweb.org/anthology/D14-1181.pdf) | + | Content-Understanding | [TagSpace](models/contentunderstanding/tagspace/model.py) | ✓ | ✓ | ✓ | x | [EMNLP 2014][TagSpace: Semantic Embeddings from Hashtags](https://www.aclweb.org/anthology/D14-1194.pdf) | + | Matching | [DSSM](models/match/dssm/model.py) | ✓ | ✓ | ✓ | x | [CIKM 2013][Learning Deep Structured Semantic Models for Web Search using Clickthrough Data](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/cikm2013_DSSM_fullversion.pdf) | + | Matching | [MultiView-Simnet](models/match/multiview-simnet/model.py) | ✓ | ✓ | ✓ | x | [WWW 2015][A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/frp1159-songA.pdf) | + | Recall | [TDM](models/treebased/tdm/model.py) | ✓ | >=1.8.0 | ✓ | >=1.8.0 | [KDD 2018][Learning Tree-based Deep Model for Recommender Systems](https://arxiv.org/pdf/1801.02294.pdf) | + | Recall | [fasttext](models/recall/fasttext/model.py) | ✓ | ✓ | x | x | [EACL 2017][Bag of Tricks for Efficient Text Classification](https://www.aclweb.org/anthology/E17-2068.pdf) | + | Recall | [Word2Vec](models/recall/word2vec/model.py) | ✓ | ✓ | ✓ | x | [NIPS 2013][Distributed Representations of Words and Phrases and their Compositionality](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf) | + | Recall | [SSR](models/recall/ssr/model.py) | ✓ | ✓ | ✓ | ✓ | [SIGIR 2016][Multi-Rate Deep Learning for Temporal Recommendation](http://sonyis.me/paperpdf/spr209-song_sigir16.pdf) | + | Recall | [Gru4Rec](models/recall/gru4rec/model.py) | ✓ | ✓ | ✓ | ✓ | [2015][Session-based Recommendations with Recurrent Neural Networks](https://arxiv.org/abs/1511.06939) | + | Recall | [Youtube_dnn](models/recall/youtube_dnn/model.py) | ✓ | ✓ | ✓ | ✓ | [RecSys 2016][Deep Neural Networks for YouTube Recommendations](https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45530.pdf) | + | Recall | [NCF](models/recall/ncf/model.py) | ✓ | ✓ | ✓ | ✓ | [WWW 2017][Neural Collaborative Filtering](https://arxiv.org/pdf/1708.05031.pdf) | + | Recall | [GNN](models/recall/gnn/model.py) | ✓ | ✓ | ✓ | ✓ | [AAAI 2019][Session-based Recommendation with Graph Neural Networks](https://arxiv.org/abs/1811.00855) | + | Ranking | [Logistic Regression](models/rank/logistic_regression/model.py) | ✓ | x | ✓ | x | / | + | Ranking | [Dnn](models/rank/dnn/model.py) | ✓ | ✓ | ✓ | ✓ | / | + | Ranking | [FM](models/rank/fm/model.py) | ✓ | x | ✓ | x | [IEEE Data Mining 2010][Factorization machines](https://analyticsconsultores.com.mx/wp-content/uploads/2019/03/Factorization-Machines-Steffen-Rendle-Osaka-University-2010.pdf) | + | Ranking | [FFM](models/rank/ffm/model.py) | ✓ | x | ✓ | x | [RECSYS 2016][Field-aware Factorization Machines for CTR Prediction](https://dl.acm.org/doi/pdf/10.1145/2959100.2959134) | + | Ranking | [FNN](models/rank/fnn/model.py) | ✓ | x | ✓ | x | [ECIR 2016][Deep Learning over Multi-field Categorical Data](https://arxiv.org/pdf/1601.02376.pdf) | + | Ranking | [Deep Crossing](models/rank/deep_crossing/model.py) | ✓ | x | ✓ | x | [ACM 2016][Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features](https://www.kdd.org/kdd2016/papers/files/adf0975-shanA.pdf) | + | Ranking | [Pnn](models/rank/pnn/model.py) | ✓ | x | ✓ | x | [ICDM 2016][Product-based Neural Networks for User Response Prediction](https://arxiv.org/pdf/1611.00144.pdf) | + | Ranking | [DCN](models/rank/dcn/model.py) | ✓ | x | ✓ | x | [KDD 2017][Deep & Cross Network for Ad Click Predictions](https://dl.acm.org/doi/pdf/10.1145/3124749.3124754) | + | Ranking | [NFM](models/rank/nfm/model.py) | ✓ | x | ✓ | x | [SIGIR 2017][Neural Factorization Machines for Sparse Predictive Analytics](https://dl.acm.org/doi/pdf/10.1145/3077136.3080777) | + | Ranking | [AFM](models/rank/afm/model.py) | ✓ | x | ✓ | x | [IJCAI 2017][Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](https://arxiv.org/pdf/1708.04617.pdf) | + | Ranking | [DeepFM](models/rank/deepfm/model.py) | ✓ | x | ✓ | x | [IJCAI 2017][DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://arxiv.org/pdf/1703.04247.pdf) | + | Ranking | [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) | + | Ranking | [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) | + | Ranking | [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) | + | Ranking | [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) | + | Ranking | [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) | + | Multi-Task | [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) | + | Multi-Task | [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) | + | Multi-Task | [ShareBottom](models/multitask/share-bottom/model.py) | ✓ | ✓ | ✓ | ✓ | [1998][Multitask learning](http://reports-archive.adm.cs.cmu.edu/anon/1997/CMU-CS-97-203.pdf) | + | Re-Rank | [Listwise](models/rerank/listwise/model.py) | ✓ | ✓ | ✓ | x | [2019][Sequential Evaluation and Generation Framework for Combinatorial Recommender System](https://arxiv.org/pdf/1902.00245.pdf) | + + + + + +

Getting Started

+ +### Environmental requirements * Python 2.7/ 3.5 / 3.6 / 3.7 * PaddlePaddle >= 1.7.2 -* 操作系统: Windows/Mac/Linux +* operating system: Windows/Mac/Linux - > Windows下目前仅提供单机训练,建议分布式使用Linux + > Linux is recommended for distributed training -### 安装命令 +### Installation -- 安装方法一 **PIP源直接安装** +1. **Install by pip** ```bash python -m pip install paddle-rec ``` - > 该方法会默认下载安装`paddlepaddle v1.7.2 cpu版本`,若提示`PaddlePaddle`无法安装,则依照下述方法首先安装`PaddlePaddle`,再安装`PaddleRec`: - > - 可以在[该地址](https://pypi.org/project/paddlepaddle/1.7.2/#files),下载PaddlePaddle后手动安装whl包 - > - 可以先pip安装`PaddlePaddle`,`python -m pip install paddlepaddle==1.7.2 -i https://mirror.baidu.com/pypi/simple` - > - 其他安装问题可以在[Paddle Issue](https://github.com/PaddlePaddle/Paddle/issues)或[PaddleRec Issue](https://github.com/PaddlePaddle/PaddleRec/issues)提出,会有工程师及时解答 + > This method will download and install`paddlepaddle-v1.7.2-cpu`,if you are prompted that `PaddlePaddle` can not be installed automatically,You need to install `PaddlePaddle` manually,and then install `Paddlerec` again: + > - Download PaddlePaddle whl from [address](https://pypi.org/project/paddlepaddle/1.7.2/#files) and install by pip. + > - Directly install `PaddlePaddle` by pip,`python -m pip install paddlepaddle==1.7.2 -i https://mirror.baidu.com/pypi/simple` + > - Other installation problems can be raised in [Paddle Issue](https://github.com/PaddlePaddle/Paddle/issues) or [PaddleRec Issue](https://github.com/PaddlePaddle/PaddleRec/issues) -- 安装方法二 **源码编译安装** +2. **Install by source code** - - 安装飞桨 **注:需要用户安装版本 == 1.7.2 的飞桨** + - Install PaddlePaddle ```shell python -m pip install paddlepaddle==1.7.2 -i https://mirror.baidu.com/pypi/simple ``` - - 源码安装PaddleRec + - Install PaddleRec by source code ``` git clone https://github.com/PaddlePaddle/PaddleRec/ @@ -107,53 +104,53 @@ python setup.py install ``` -- PaddleRec-GPU安装方法 +- Install PaddleRec-GPU - 在使用方法一或方法二完成PaddleRec安装后,需再手动安装`paddlepaddle-gpu`,并根据自身环境(Cuda/Cudnn)选择合适的版本,安装教程请查阅[飞桨-开始使用](https://www.paddlepaddle.org.cn/install/quick) + After installing `PaddleRec`,You need to manually install `paddlepaddle-gpu`,select the appropriate version according to your environment (CUDA / cudnn),please refer to the installation tutorial[Installation Manuals](https://www.paddlepaddle.org.cn/documentation/docs/en/install/index_en.html) -

一键启动

+

Quick Start

-我们以排序模型中的`dnn`模型为例介绍PaddleRec的一键启动。训练数据来源为[Criteo数据集](https://www.kaggle.com/c/criteo-display-ad-challenge/),我们从中截取了100条数据: +We take the `dnn` algorithm as an example to introduce the quick start of `PaddleRec`, and we took 100 pieces of training data from [Criteo Dataset](https://www.kaggle.com/c/criteo-display-ad-challenge/): ```bash -# 使用CPU进行单机训练 +# Training with cpu python -m paddlerec.run -m paddlerec.models.rank.dnn ``` -

帮助文档

+

Documentation

-### 项目背景 -* [推荐系统介绍](doc/rec_background.md) -* [分布式深度学习介绍](doc/ps_background.md) +### Background +* [Recommendation System](doc/rec_background.md) +* [Distributed deep learning](doc/ps_background.md) -### 快速开始 -* [十分钟上手PaddleRec](https://aistudio.baidu.com/aistudio/projectdetail/559336) +### Introductory Project +* [Ten minutes to learn PaddleRec](https://aistudio.baidu.com/aistudio/projectdetail/559336) -### 入门教程 -* [数据准备](doc/slot_reader.md) -* [模型调参](doc/model.md) -* [启动训练](doc/train.md) -* [启动预测](doc/predict.md) -* [快速部署](doc/serving.md) +### Introductory tutorial +* [Prepare Data](doc/slot_reader.md) +* [HyperParameter of model](doc/model.md) +* [Start Training](doc/train.md) +* [Start Predicting](doc/predict.md) +* [Serving](doc/serving.md) -### 进阶教程 -* [自定义Reader](doc/custom_reader.md) -* [自定义模型](doc/model_develop.md) -* [自定义流程](doc/trainer_develop.md) -* [yaml配置说明](doc/yaml.md) -* [PaddleRec设计文档](doc/design.md) +### Advanced tutorial +* [Custom Reader](doc/custom_reader.md) +* [Custom Model](doc/model_develop.md) +* [Custom Training Process](doc/trainer_develop.md) +* [Configuration description of yaml](doc/yaml.md) +* [Design document of PaddleRec](doc/design.md) ### Benchmark * [Benchmark](doc/benchmark.md) ### FAQ -* [常见问题FAQ](doc/faq.md) +* [Common Problem FAQ](doc/faq.md) -

社区

+

Community


@@ -163,22 +160,22 @@ python -m paddlerec.run -m paddlerec.models.rank.dnn

-### 版本历史 +### Version history - 2020.06.17 - PaddleRec v0.1.0 - 2020.06.03 - PaddleRec v0.0.2 - 2020.05.14 - PaddleRec v0.0.1 -### 许可证书 -本项目的发布受[Apache 2.0 license](LICENSE)许可认证。 +### License +[Apache 2.0 license](LICENSE) -### 联系我们 +### Contack us -如有意见、建议及使用中的BUG,欢迎在[GitHub Issue](https://github.com/PaddlePaddle/PaddleRec/issues)提交 +For any feedback or to report a bug, please propose a [GitHub Issue](https://github.com/PaddlePaddle/PaddleRec/issues) -亦可通过以下方式与我们沟通交流: +You can also communicate with us in the following ways: -- QQ群号码:`861717190` -- 微信小助手微信号:`paddlerec2020` +- QQ group id:`861717190` +- Wechat account:`paddlerec2020`

     

-

PaddleRec交流QQ群               PaddleRec微信小助手

+

PaddleRec QQ Group               PaddleRec Wechat account

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