Awesome

DICE

This is the official implementation of our WWW'21 paper:

Yu Zheng, Chen Gao, Xiang Li, Xiangnan He, Depeng Jin, Yong Li, Disentangling User Interest and Conformity for Recommendation with Causal Embedding, In Proceedings of the Web Conference 2021.

Model training

First unzip the datasets and start the visdom server:

visdom -port 33336

Then simply run the following command to reproduce the experiments on corresponding dataset and model:

python app.py --flagfile ./config/xxx.cfg

Embedding visualization

The visualization codes to reproduce Figure 5(b) and Figure 7 can be found in the viz folder.

First, reduce the dimension of the embedding vectors to 2D using t-SNE (remember to change the path to the model checkpoint in viz.py):

python viz.py

Then, visualize the 2D embedding vectors using MATLAB:

embedding_viz.m

Dataset processing

The dataset process codes are in this repo. Please check this issue for more details.

Citation

If you use our codes and datasets in your research, please cite:

@inproceedings{zheng2021disentangling,
  title={Disentangling User Interest and Conformity for Recommendation with Causal Embedding},
  author={Zheng, Yu and Gao, Chen and Li, Xiang and He, Xiangnan and Li, Yong and Jin, Depeng},
  booktitle={Proceedings of the Web Conference 2021},
  pages={2980--2991},
  year={2021}
}