https://github.com/karapostk/modprotodebias

This repository hosts the code and the settings for the paper "Modular Debiasing of Latent User Representations in Prototype-based Recommender Systems" by Alessandro B. Melchiorre, Shahed Masoudian, Deepak Kumar, and Markus Schedl at ECML-PKDD'24.
https://github.com/karapostk/modprotodebias

debiasing-recommendation explainable-ai explainable-recommendation recommeder-systems

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This repository hosts the code and the settings for the paper "Modular Debiasing of Latent User Representations in Prototype-based Recommender Systems" by Alessandro B. Melchiorre, Shahed Masoudian, Deepak Kumar, and Markus Schedl at ECML-PKDD'24.

• Host: GitHub
• URL: https://github.com/karapostk/modprotodebias
• Owner: karapostK
• License: mit
• Created: 2024-02-09T15:31:08.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-08-27T19:43:37.000Z (almost 2 years ago)
• Last Synced: 2024-08-27T21:43:06.863Z (almost 2 years ago)
• Topics: debiasing-recommendation, explainable-ai, explainable-recommendation, recommeder-systems
• Language: Python
• Homepage:
• Size: 2 MB
• Stars: 0
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Modular Debiasing of Latent User Representations in Prototype-based Recommender Systems @ ECML-PKDD'24

This repository hosts the code and and the settings for the paper ["Modular Debiasing of Latent User Representations in Prototype-based Recommender Systems"](https://karapostk.github.io/assets/pdf/melchiorre2024modular.pdf) by [Alessandro B. Melchiorre](https://karapostk.github.io/), Shahed Masoudian, Deepak Kumar, and Markus Schedl at ECML-PKDD'24.

![MMD_Debiasing](./results/plots/gender_lfm2bdemobias_mmd_bacc_ndcg_lams.png "MMD Gender Debiasing")

## Installation

### Environment

- Install the environment with

  `conda env create -f modprotodebias.yml`

- Activate the environment with `conda activate modprotodebias`

### Data

- move into the folder with `cd data/`

- run `python _processor.py`

If you have problems with the LFM2b data, ping me and I'll be happy to help

### Pre-Trained Models

- download the pre-trained ProtoMF models from [here](https://drive.jku.at/filr/public-link/file-download/0cce88f0905932a10190c68ce5731feb/62214/-7633188943201829349/pre_trained_models.zip)

- place the two folders inside `pre_trained_models` folder (default)

- (optional) adjust the path files in the `conf.yml` if you have issues

## Usage

Adjust the configuration of your experiment in `run_full_debiasing.py`.

The experiments can be started with

`python start.py run_full_debiasing`

or define sweep configurations to use with the wandb sweep command

`wandb sweep sweep_config.yaml`

## Cite

```latex

@inproceedings{melchiorre2024modular,

  title = {Modular Debiasing of Latent User Representations in Prototype-based Recommender Systems},

  author = {Melchiorre, Alessandro B. and Masoudian, Shahed and Kumar, Deepak and Schedl, Markus},

  booktitle = {Proceedings of 2024 Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD)},

  year = {2024},

}

```

## License

The code in this repository is licensed under the MIT License. For details, please see the LICENSE file.

## Acknowledgments

This research was funded in whole or in part by the Austrian Science Fund (FWF): P36413, P33526, and DFH-23, and by the State of Upper Austria and the Federal Ministry of Education, Science, and Research, through grant LIT-2021-YOU-215.