shelveyj / iclrec

Intent Contrastive Learning for Sequential Recommendation (WWW'22)

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

Datasets

Four prepared datasets are included in data folder.

Train Model

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

python main.py --data_name Sports_and_Outdoors

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

Evaluate Model

You can directly evaluate a trained model on test set by running:

python main.py --data_name Sports_and_Outdoors --model_idx 0 --do_eval

We provide a model that trained on Sports_and_Games dataset in ./src/output folder. Please feel free to test is out.

Acknowledgment

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