README_en.md

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<img align="center" src="doc/imgs/logo.png">
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<img align="center" src="doc/imgs/structure.png">
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<img align="center" src="doc/imgs/overview.png">
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<h2 align="center">What is recommendation system ?</h2>
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<img align="center" src="doc/imgs/rec-overview.png">
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- Recommendation system is the key to help users get information of interest efficiently in the era of explosive growth of Internet information

- The recommendation system is also a silver bullet to help the product attract users, retain users, increase user stickiness and improve user conversion.

- Excellent recommendation system can help the product establish a good reputation, and help the product gain market share

  > It can be said that who can master and make good use of the recommendation system, who can get the first chance in the fierce competition of information distribution.
  >
  > At the same time, there are many problems that perplex the developers of the recommendation system, such as: huge data volume, complex model structure, inefficient distributed training environment, demanding online deployment requirements, all of which are too numerous to enumerate.

<h2 align="center">What is PaddleRec ?</h2>


- 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)                                                                           |





<h2 align="center">快速安装</h2>

### 环境要求
* Python 2.7/ 3.5 / 3.6 / 3.7
* PaddlePaddle  >= 1.7.2
* 操作系统: Windows/Mac/Linux

  > Windows下目前仅提供单机训练，建议分布式使用Linux
  
### 安装命令

- 安装方法一 **PIP源直接安装**
  ```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

- 安装方法二 **源码编译安装**
  
  - 安装飞桨  **注：需要用户安装版本 == 1.7.2 的飞桨**

    ```shell
    python -m pip install paddlepaddle==1.7.2 -i https://mirror.baidu.com/pypi/simple
    ```

  - 源码安装PaddleRec

    ```
    git clone https://github.com/PaddlePaddle/PaddleRec/
    cd PaddleRec
    python setup.py install
    ```

- PaddleRec-GPU安装方法

  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)


<h2 align="center">一键启动</h2>

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：

```bash
# 使用CPU进行单机训练
python -m paddlerec.run -m paddlerec.models.rank.dnn  
```


<h2 align="center">帮助文档</h2>

### 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)

### 快速开始
* [十分钟上手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)


### 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)

### Benchmark
* [Benchmark](doc/benchmark.md)

### FAQ
* [常见问题FAQ](doc/faq.md)


<h2 align="center">社区</h2>

<p align="center">
    <br>
    <img alt="Release" src="https://img.shields.io/badge/Release-0.1.0-yellowgreen">
    <img alt="License" src="https://img.shields.io/github/license/PaddlePaddle/PaddleRec">
    <img alt="Slack" src="https://img.shields.io/badge/Join-Slack-green">
    <br>
<p>

### 版本历史
- 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。

### 联系我们

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

亦可通过以下方式与我们沟通交流：

- QQ群号码：`861717190`
- 微信小助手微信号：`paddlerec2020`

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