Learning Multi-Faceted Prototypical User Interests

This repository is the official implementation of paper

Nhu-Thuat Tran and Hady W. Lauw. 2024. Learning Multi-Faceted Prototypical User Interests. In Proceedings of 12th International Conference on Learning Representations (ICLR'24), Vienna, Austria, May 7-11, 2024.

Environments

• Anaconda: 4.12.0
• Python: 3.7.5
• OS: MacOS

Data

Prepare the data as samples, i.e., tab-separated csv format. part1, part2, part3 are training, validation and test splits, respectively. For more information on data format, please visit RecBole v1.1 documentation.

Requirements

To create virtual environment

conda create --prefix ./myenv python=3.7.5 -y

Then activate environment

conda activate ./myenv

To install requirements

pip install -r requirements.txt

Training and Evaluation

1. Create a YAML config file under configs folder as samples.

2. Prepare run.sh file as follows

python run_facetvae.py --config_file <your_config_file> --device_id <ID of GPU machine>

3. To run training and evaluation

bash run.sh

Notes on RecBole setting to reproduce results

The default setting of RecBole 1.1 is to exclude user historical interactions in training and validation sets when evaluating on test set. However, some existing baselines only exclude user historical interactions in training set when evaluating on test set. Thus, for fair comparison, after installing RecBole, change line 264 RecBole/recbole/data/utils.py to

test_sampler = sampler.set_phase('train')
test_sampler.phase = 'test'

Citation

If you find our work useful for your research, please cite our paper as

@inproceedings{FacetVAE,
    title={Learning Multi-Faceted Prototypical User Interests},
    author={Nhu-Thuat Tran and Hady W. Lauw},
    booktitle={The Twelfth International Conference on Learning Representations},
    year={2024},
    url={https://openreview.net/forum?id=MzjiMxlWab}
}