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All Projects → domkowald → LFM1b-analyses

domkowald / LFM1b-analyses

Licence: GPL-3.0 license
Python scripts for studying bias in recommender systems

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Jupyter Notebook
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biasfairnessrecommendersystems

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FairRecSys

This repository provides Python scripts for studying fairness and popularity bias in (multimedia) recommender systems.

LFM_fairness.ipynb

This i-python notebook reproduces the "Unfairness in recommender systems" analyzes of https://arxiv.org/pdf/1907.13286v1.pdf in the context of music recommender systems using a subset of the LFM-1b dataset. This reproducibility work was accepted at ECIR'2020 and is available via https://arxiv.org/pdf/1912.04696.pdf.

For executing it, simply download the dataset from https://zenodo.org/record/3475975#.XZ7i1mbgpPY and copy the files into the "data" folder. All other instructions are given in the notebook itself.

MMRS_fairness.ipynb

This i-python notebook enables to evalute the fairness in multimedia recommender systems (MMRS) datasets available at: https://zenodo.org/record/6123879#.Yg-FRpYxmUk

For executing it, please copy the files into the "data" folder and follow the instructions in the notebook itself.

Requirements

  • Python 3
  • Jupyter
  • Pandas
  • Matplotlib
  • Surprise
  • Numpy
  • Scipy
  • Sklearn

All these packages can be easily installed using https://www.anaconda.com/

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