PyTorch library for recommender systems
Project description
FreeRec is a repository designed for easy (recommendation) data pre-processing and model training. I am a beginner in the field of recommender systems, so much of FreeRec's designs may not be as effective. In addition, you are free to specify your own framework based on FreeRec.
Requirements:
Python >= 3.9 | PyTorch >=2.0 | TorchData >=0.6.0 | PyG >=2.3
conda create --name=FreeRec python=3.9
conda activate FreeRec
Note: After PyTorch 2.0, TorchData
seems to have stopped being updated, and you can install it with --no-deps
to avoid installing dependencies.
pip install --no-deps torchdata
Installation
pip install freerec
or (for latest)
pip install git+https://github.com/MTandHJ/freerec.git
Data Pipeline
Note: To make dataset, please download corresponding Atomic files from [RecBole].
Then, run freerec make --help
.
Training Flow
Reference Code
- TorchRec: https://github.com/pytorch/torchrec
- DeepCTR-Torch: https://github.com/shenweichen/DeepCTR-Torch
- FuxiCTR: https://github.com/xue-pai/FuxiCTR
- BARS: https://github.com/openbenchmark/BARS
- RecBole: https://github.com/RUCAIBox/RecBole
Acknowledgements
Thanks to ChatGPT for the annotation of some code. For this reason, some of the comments may be illogical.
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