(简体中文|English)
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Recommendation system helps users quickly find useful and interesting information from massive data.
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Recommendation system is also a silver bullet to attract users, retain users, increase users' stickness or conversionn.
Who can better use the recommendation system, who can gain more advantage in the fierce competition.
At the same time, there are many problems in the process of using the recommendation system, such as: huge data, complex model, inefficient distributed training, and so on.
- A quick start tool of search & recommendation algorithm based on PaddlePaddle
- A complete solution of recommendation system for beginners, developers and researchers.
- Recommendation algorithm library including content-understanding, match, recall, rank, multi-task, re-rank etc.
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Python 2.7/ 3.5 / 3.6 / 3.7 , Python 3.7 is recommended ,Python in example represents Python 3.7 by default
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PaddlePaddle >=2.0
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operating system: Windows/Mac/Linux
Linux is recommended for distributed training
- Install by pip in GPU environment
python -m pip install paddlepaddle-gpu==2.0.0
- Install by pip in CPU environment
python -m pip install paddlepaddle # gcc8
For download more versions, please refer to the installation tutorial Installation Manuals
git clone https://github.com/PaddlePaddle/PaddleRec/
cd PaddleRec
We take the dnn
algorithm as an example to get start of PaddleRec
, and we take 100 pieces of training data from Criteo Dataset:
python -u tools/trainer.py -m models/rank/dnn/config.yaml # Training with dygraph model
python -u tools/static_trainer.py -m models/rank/dnn/config.yaml # Training with static model
- Custom Reader
- Custom Model
- Configuration description of yaml
- Training visualization
- Serving
- Python inference
- Benchmark
- 2021.05.19 - PaddleRec v2.1.0
- 2021.01.29 - PaddleRec v2.0.0
- 2020.10.12 - PaddleRec v1.8.5
- 2020.06.17 - PaddleRec v0.1.0
- 2020.06.03 - PaddleRec v0.0.2
- 2020.05.14 - PaddleRec v0.0.1
For any feedback, please propose a GitHub Issue
You can also communicate with us in the following ways:
- QQ group id:
861717190
- Wechat account:
wxid_0xksppzk5p7f22
- Remarks
REC
add group automatically
PaddleRec QQ Group PaddleRec Wechat account