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.
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 == 1.7.3
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.
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
@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 (SIGIR)},
year={2023}
}