Disentangled Contrastive Collaborative Filtering

This is the PyTorch implementation by @Re-bin for DCCF model proposed in this paper:

Disentangled Contrastive Collaborative Filtering
Xubin Ren, Lianghao Xia, Jiashu Zhao, Dawei Yin, Chao Huang*
SIGIR 2023

* denotes corresponding author

In this paper, we propose a disentangled contrastive learning method for recommendation, which explores latent factors underlying implicit intents for interactions. In particular, a graph structure learning layer is devised to enable the adaptive interaction augmentation, based on the learned disentangle user (item) intent-aware dependencies. Along the augmented intent-aware graph structures, we propose a intent-aware contrastive learning scheme to bring the benefits of disentangled self-supervision signals.

Environment

The codes are written in Python 3.8.13 with the following dependencies.

• numpy == 1.22.3
• pytorch == 1.11.0 (GPU version)
• torch-scatter == 2.0.9
• torch-sparse == 0.6.14
• scipy == 1.9.3

Dataset

We utilized three public datasets to evaluate DCCF: Gowalla, Amazon-book, and Tmall.

Note that the validation set is only used for tuning hyperparameters, and for Gowalla / Tmall, the validation set is merged into the training set for training.

Examples to run the codes

The command to train DCCF on the Gowalla / Amazon-book / Tmall dataset is as follows.

We train DCCF with a fixed number of epochs and save the parameters obtained after the final epoch for testing.

• Gowalla

  python DCCF_PyTorch.py --dataset gowalla --epoch 150

• Amazon-book:

  python DCCF_PyTorch.py --dataset amazon --epoch 100

• Tmall:

  python DCCF_PyTorch.py --dataset tmall --epoch 100

For advanced usage of arguments, run the code with --help argument.

Thanks for your interest in our work.

Citation

If you find this work is helpful to your research, please consider citing our paper:

@inproceedings{ren2023disentangled,
  title={Disentangled contrastive collaborative filtering},
  author={Ren, Xubin and Xia, Lianghao and Zhao, Jiashu and Yin, Dawei and Huang, Chao},
  booktitle={Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages={1137--1146},
  year={2023}
}