Intent Contrastive Learning for Sequential Recommendation (ICLRec)

Source code for paper: Intent Contrastive Learning for Sequential Recommendation

Introduction

Motivation:

Users' interactions with items are driven by various underlying intents. These intents are often unobservable while potentially beneficial to learn a better users' preferences toward massive item set.

Model Architecture:

Reference

Please cite our paper if you use this code.

@article{chen2022intent,
  title={Intent Contrastive Learning for Sequential Recommendation},
  author={Chen, Yongjun and Liu, Zhiwei and Li, Jia and McAuley, Julian and Xiong, Caiming},
  journal={arXiv preprint arXiv:2202.02519},
  year={2022}
}

Implementation

Requirements

Python >= 3.7
Pytorch >= 1.2.0
tqdm == 4.26.0 faiss-gpu==1.7.1

Datasets

Four prepared datasets are included in data folder.

Evaluate Model

We provide the trained models on Beauty, Sports_and_Games, Toys_and_Games, and Yelp datasets in ./src/output folder. You can directly evaluate the trained models on test set by running:

python main.py --data_name <Data_name> --model_idx 1 --do_eval

You are expected following results:

On Beauty:

{'Epoch': 0, 'HIT@5': '0.0500', 'NDCG@5': '0.0326', 'HIT@10': '0.0744', 'NDCG@10': '0.0403', 'HIT@20': '0.1058', 'NDCG@20': '0.0483'}

On Sports:

{'Epoch': 0, 'HIT@5': '0.0290', 'NDCG@5': '0.0191', 'HIT@10': '0.0437', 'NDCG@10': '0.0238', 'HIT@20': '0.0646', 'NDCG@20': '0.0291'}

On Toys:

{'Epoch': 0, 'HIT@5': '0.0598', 'NDCG@5': '0.0404', 'HIT@10': '0.0834', 'NDCG@10': '0.0480', 'HIT@20': '0.1138', 'NDCG@20': '0.0557'}

On Yelp:

{'Epoch': 0, 'HIT@5': '0.0240', 'NDCG@5': '0.0153', 'HIT@10': '0.0409', 'NDCG@10': '0.0207', 'HIT@20': '0.0659', 'NDCG@20': '0.0270'}

Please feel free to test is out!

Train Model

To train ICLRec on a specific dataset, change to the src folder and run following command:

bash scripts/run_<data_name>.sh

The script will automatically train ICLRec and save the best model found in validation set, and then evaluate on test set.

Acknowledgment

Transformer and training pipeline are implemented based on S3-Rec. Thanks them for providing efficient implementation.

ytchx1999 / ICLRec