EQNet: A Post-Processing Approach to Manage Popularity Bias in Collaborative Filter Recommender Systems

Gabriel Machado, Wladmir Brandão, Humberto Marques-Neto

2024

Abstract

Recommendation systems play a pivotal role in digital platforms, facilitating novel user experiences by effectively sorting and presenting items that align with their preferences. However, these systems often suffer from popularity bias, a phenomenon characterized by the algorithm’s inclination to favor a few popular items, resulting in the under-representation of the vast majority of items. Addressing this bias and enhancing the recommendation of long-tail items is of utmost importance. In this paper, we propose the EQNet, a re-ranking approach designed to mitigate popularity bias and improve the recommendation quality of an SVD-based recommendation system. EQNet leverages PageRank or Popularity Count outputs to re-rank items, and its effectiveness is evaluated using four metrics: average popularity, percentage of long-tailed items, coverage of long-tailed items, and recommendation quality. We incorporate the widely recognized bias mitigation algorithm FA*IR into our experimentation to establish a robust baseline. By comparing the performance of EQNet against this state-of-the-art approach, we show the efficiency of EQNet and highlight its potential to enhance existing methods for mitigating popularity bias.

Paper Citation

in Harvard Style

Machado G., Brandão W. and Marques-Neto H. (2024). EQNet: A Post-Processing Approach to Manage Popularity Bias in Collaborative Filter Recommender Systems. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 919-932. DOI: 10.5220/0012612800003690

in Bibtex Style

@conference{iceis24,
author={Gabriel Machado and Wladmir Brandão and Humberto Marques-Neto},
title={EQNet: A Post-Processing Approach to Manage Popularity Bias in Collaborative Filter Recommender Systems},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2024},
pages={919-932},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012612800003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - EQNet: A Post-Processing Approach to Manage Popularity Bias in Collaborative Filter Recommender Systems
SN - 978-989-758-692-7
AU - Machado G.
AU - Brandão W.
AU - Marques-Neto H.
PY - 2024
SP - 919
EP - 932
DO - 10.5220/0012612800003690
PB - SciTePress