- 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 useful infomation from big data.
-The recommendation system is also a silver bullet to help the product attract users, retain users, increase user stickiness and improve user conversion.
-Recommendation system is also a silver bullet to 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.
> Who can better use the recommendation system, who can gain more advantage in the fierce competition.
>
> 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.
> At the same time, there are many problems in the process of using the recommendation system, such as: huge data volume, complex model structure, inefficient distributed training environment, demanding online deployment requirements, and so on.
<h2align="center">What is PaddleRec ?</h2>
- 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.
- A quick start tool of search & recommendation algorithm based on [PaddlePaddle](https://www.paddlepaddle.org.cn/documentation/docs/en/beginners_guide/index_en.html)
- The complete solution of recommendation system for beginners, developers and researchers
-Recommendation algorithm library including content-understanding, matching, recall, ranking, multi-task, re-rank etc.
| Type | Algorithm | CPU | GPU | Parameter-Server | Multi-GPU | Paper |
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@@ -84,9 +82,9 @@
```bash
python -m pip install paddle-rec
```
> 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.
> This method will download and install `paddlepaddle-v1.7.2-cpu`. If `PaddlePaddle` can not be installed automatically，You need to install `PaddlePaddle` manually，and then install `PaddleRec` again：
> - Download [PaddlePaddle](https://pypi.org/project/paddlepaddle/1.7.2/#files) and install by pip.
> - Other installation problems can be raised in [Paddle Issue](https://github.com/PaddlePaddle/Paddle/issues) or [PaddleRec Issue](https://github.com/PaddlePaddle/PaddleRec/issues)
2.**Install by source code**
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@@ -107,12 +105,12 @@
- Install PaddleRec-GPU
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)
After installing `PaddleRec`，please install the appropriate version of `paddlepaddle-gpu` according to your environment (CUDA / cudnn)，refer to the installation tutorial [Installation Manuals](https://www.paddlepaddle.org.cn/documentation/docs/en/install/index_en.html)
<h2align="center">Quick Start</h2>
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/):
We take the `dnn` algorithm as an example to get start of `PaddleRec`, and we take 100 pieces of training data from [Criteo Dataset](https://www.kaggle.com/c/criteo-display-ad-challenge/):
