A pytorch implementation of CL4SRec in "Contrastive Learning for Sequential Recommendation", which provides three output aggregation strategies including 'concat', 'mean' and 'predict' and three augmentation strategies 'mask', 'reorder' and 'crop'.

The dataset should be organized as the following format. The first column is the userid, followed by the interacted items.

# ./data/dataset_name.txt
user item1 item2 ...

You can train CL4SRec on Yelp dataset by following command

python -u main.py --dataset Yelp --cl_embs predict

The Transformer layer is implemented based on recbole.