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.