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README.md
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(简体中文|[English](./README_en.md))
<p align="center"> <p align="center">
<img align="center" src="doc/imgs/logo.png"> <img align="center" src="doc/imgs/logo.png">
<p> <p>
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| 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 | [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 | [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 | [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) | | 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 | [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 | [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) | | 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) |
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README_en.md
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<img align="center" src="doc/imgs/logo.png"> <img align="center" src="doc/imgs/logo.png">
<p> <p>
<p align="center"> <p align="center">
<img align="center" src="doc/imgs/structure.png"> <img align="center" src="doc/imgs/overview_en.png">
<p>
<p align="center">
<img align="center" src="doc/imgs/overview.png">
<p> <p>
<h2 align="center">What is recommendation system ?</h2> <h2 align="center">What is recommendation system ?</h2>
<p align="center"> <p align="center">
<img align="center" src="doc/imgs/rec-overview.png"> <img align="center" src="doc/imgs/rec-overview-en.png">
<p> <p>
- Recommendation system is the key to help users get information of interest efficiently in the era of explosive growth of Internet information - Recommendation system is the key to help users get information of interest efficiently in the era of explosive growth of Internet information
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<h2 align="center">What is PaddleRec ?</h2> <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** - A quick start tool of search & recommendation model based on [PaddlePaddle](https://www.paddlepaddle.org.cn/documentation/docs/en/beginners_guide/index_en.html)
- 适合初学者，开发者，研究者的推荐系统全流程解决方案 - The whole process solution of recommendation system for beginners, developers and researchers
- 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 - Complete recommendation algorithm library including content understanding, matching, recall, ranking, multi-task, re-rank etc.
| 方向 | 模型 | 单机CPU | 单机GPU | 分布式CPU | 分布式GPU | 论文 | | Type | Algorithm | CPU | GPU | Parameter-Server | Multi-GPU | Paper |
| :------: | :-----------------------------------------------------------------------: | :-----: | :-----: | :-------: | :-------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | :-------------------: | :-----------------------------------------------------------------------: | :---: | :-----: | :--------------: | :-------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 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) | | 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) |
| 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) | | Content-Understanding | [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) | | 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) |
| 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) | | 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) |
| 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) | | 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) |
| 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) | | 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) |
| 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) | | 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) |
| 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) | | Recall | [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) | | Recall | [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) | | 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) |
| 召回 | [NCF](models/recall/ncf/model.py) | ✓ | ✓ | ✓ | ✓ | [WWW 2017][Neural Collaborative Filtering](https://arxiv.org/pdf/1708.05031.pdf) | | Recall | [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) | | Recall | [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 | / | | Ranking | [Logistic Regression](models/rank/logistic_regression/model.py) | ✓ | x | ✓ | x | / |
| 排序 | [Dnn](models/rank/dnn/model.py) | ✓ | ✓ | ✓ | ✓ | / | | Ranking | [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) | | 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) |
| 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) | | 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) |
| 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) | | 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) |
| 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) | | 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) |
| 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) | | 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) |
| 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) | | 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) |
| 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) | | 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) |
| 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) | | 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) |
| 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) | | 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) |
| 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) | | 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) |
| 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) | | 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) |
| 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) | | 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) |
| 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) | 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) |
| 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) | | 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) |
| 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) | | 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) |
| 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) | | 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) |
| 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) | | 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) |
| 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) | | 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) |
<h2 align="center">快速安装</h2> <h2 align="center">Getting Started</h2>
### 环境要求 ### Environmental requirements
* Python 2.7/ 3.5 / 3.6 / 3.7 * Python 2.7/ 3.5 / 3.6 / 3.7
* PaddlePaddle >= 1.7.2 * 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 ```bash
python -m pip install paddle-rec 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`： > 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：
> - 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 > - Download PaddlePaddle whl from [address](https://pypi.org/project/paddlepaddle/1.7.2/#files) and install by pip.
> - 可以先pip安装`PaddlePaddle`，`python -m pip install paddlepaddle==1.7.2 -i https://mirror.baidu.com/pypi/simple` > - Directly install `PaddlePaddle` by pip，`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 > - 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 ```shell
python -m pip install paddlepaddle==1.7.2 -i https://mirror.baidu.com/pypi/simple 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/ git clone https://github.com/PaddlePaddle/PaddleRec/
...@@ -107,53 +104,53 @@ ...@@ -107,53 +104,53 @@
python setup.py install 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)
<h2 align="center">一键启动</h2> <h2 align="center">Quick Start</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： 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 ```bash
# 使用CPU进行单机训练 # Training with cpu
python -m paddlerec.run -m paddlerec.models.rank.dnn python -m paddlerec.run -m paddlerec.models.rank.dnn
``` ```
<h2 align="center">帮助文档</h2> <h2 align="center">Documentation</h2>
### 项目背景 ### Background
* [推荐系统介绍](doc/rec_background.md) * [Recommendation System](doc/rec_background.md)
* [分布式深度学习介绍](doc/ps_background.md) * [Distributed deep learning](doc/ps_background.md)
### 快速开始 ### Introductory Project
* [十分钟上手PaddleRec](https://aistudio.baidu.com/aistudio/projectdetail/559336) * [Ten minutes to learn PaddleRec](https://aistudio.baidu.com/aistudio/projectdetail/559336)
### 入门教程 ### Introductory tutorial
* [数据准备](doc/slot_reader.md) * [Prepare Data](doc/slot_reader.md)
* [模型调参](doc/model.md) * [HyperParameter of model](doc/model.md)
* [启动训练](doc/train.md) * [Start Training](doc/train.md)
* [启动预测](doc/predict.md) * [Start Predicting](doc/predict.md)
* [快速部署](doc/serving.md) * [Serving](doc/serving.md)
### 进阶教程 ### Advanced tutorial
* [自定义Reader](doc/custom_reader.md) * [Custom Reader](doc/custom_reader.md)
* [自定义模型](doc/model_develop.md) * [Custom Model](doc/model_develop.md)
* [自定义流程](doc/trainer_develop.md) * [Custom Training Process](doc/trainer_develop.md)
* [yaml配置说明](doc/yaml.md) * [Configuration description of yaml](doc/yaml.md)
* [PaddleRec设计文档](doc/design.md) * [Design document of PaddleRec](doc/design.md)
### Benchmark ### Benchmark
* [Benchmark](doc/benchmark.md) * [Benchmark](doc/benchmark.md)
### FAQ ### FAQ
* [常见问题FAQ](doc/faq.md) * [Common Problem FAQ](doc/faq.md)
<h2 align="center">社区</h2> <h2 align="center">Community</h2>
<p align="center"> <p align="center">
<br> <br>
...@@ -163,22 +160,22 @@ python -m paddlerec.run -m paddlerec.models.rank.dnn ...@@ -163,22 +160,22 @@ python -m paddlerec.run -m paddlerec.models.rank.dnn
<br> <br>
<p> <p>
### 版本历史 ### Version history
- 2020.06.17 - PaddleRec v0.1.0 - 2020.06.17 - PaddleRec v0.1.0
- 2020.06.03 - PaddleRec v0.0.2 - 2020.06.03 - PaddleRec v0.0.2
- 2020.05.14 - PaddleRec v0.0.1 - 2020.05.14 - PaddleRec v0.0.1
### 许可证书 ### License
本项目的发布受[Apache 2.0 license](LICENSE)许可认证。 [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` - QQ group id：`861717190`
- 微信小助手微信号：`paddlerec2020` - Wechat account：`paddlerec2020`
<p align="center"><img width="200" height="200" margin="500" src="./doc/imgs/QQ_group.png"/>&#8194;&#8194;&#8194;&#8194;&#8194<img width="200" height="200" src="doc/imgs/weixin_supporter.png"/></p> <p align="center"><img width="200" height="200" margin="500" src="./doc/imgs/QQ_group.png"/>&#8194;&#8194;&#8194;&#8194;&#8194<img width="200" height="200" src="doc/imgs/weixin_supporter.png"/></p>
<p align="center">PaddleRec交流QQ群&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;PaddleRec微信小助手</p> <p align="center">PaddleRec QQ Group&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;PaddleRec Wechat account</p>
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