RecBole-DA

RecBole-DA is a library built upon PyTorch and RecBole for reproducing and developing data augmentation for sequential recommendation.

1）Highlights

• Easy-to-use API: Our library provides extensive API based on common data augmentation strategies, users can further develop own new models based on our library.
• Full Coverage of Classic Methods: We provide seven data augmentation methods based on recommender systems in three major categories.

2）Implemented Models

Our library includes algorithms covering three major categories:

• Heuristic-based Methods: CL4SRec, DuoRec
• Model-based Methods: MMInfoRec, CauseRec
• Hybird Methods: CASR, CCL, CoSeRec

3）Requirements

recbole>=1.0.0
pytorch>=1.7.0
python>=3.7.0

4）Quick-Start

With the source code, you can use the provided script for initial usage of our library:

python run_seq.py --dataset='ml-1m' --train_batch_size=256 lmd=0.1 --lmd_sem=0.1 --model='CL4SRec' --contrast='us_x' --sim='dot' --tau=1

If you want to change the models or datasets, just run the script by setting additional command parameters:

python run_seq.py -m [model] -d [dataset]

5）The Team

RecBole-DA is developed and maintained by members from RUCAIBox, the developer is Shuqing Bian (@fancybian).

6） Acknowledgement

CoSeRec and CauseRec are implemented based on CoSeRec and CauseRec. Thanks them for providing efficient implementation.