Counteracting Popularity-Bias and Improving Diversity Through Calibrated Recommendations

Andre Sacilotti, Rodrigo Souza, Marcelo G. Manzato

2023

Abstract

Calibration is one approach to dealing with unfairness and popularity bias in recommender systems. While popularity bias can shift users towards consuming more mainstream items, unfairness can harm certain users by not recommending items according to their preferences. However, most state-of-art works on calibration focus only on providing fairer recommendations to users, not considering the popularity bias, which can amplify the long tail effect. To fill the research gap, in this work, we propose a calibration approach that aims to meet users’ interests according to different levels of the items’ popularity. In addition, the system seeks to reduce popularity bias and increase the diversity of recommended items. The proposed method works in a post-processing step and was evaluated through metrics that analyze aspects of fairness, popularity, and accuracy through an offline experiment with two different datasets. The system’s efficiency was validated and evaluated with three different recommendation algorithms, verifying which behaves better and comparing the performance with four other state-of-the-art calibration approaches. As a result, the proposed technique reduced popularity bias and increased diversity and fairness in the two datasets considered.

Paper Citation

in Harvard Style

Sacilotti A., Souza R. and G. Manzato M. (2023). Counteracting Popularity-Bias and Improving Diversity Through Calibrated Recommendations. In Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-648-4, SciTePress, pages 709-720. DOI: 10.5220/0011846000003467

in Bibtex Style

@conference{iceis23,
author={Andre Sacilotti and Rodrigo Souza and Marcelo G. Manzato},
title={Counteracting Popularity-Bias and Improving Diversity Through Calibrated Recommendations},
booktitle={Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2023},
pages={709-720},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011846000003467},
isbn={978-989-758-648-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Counteracting Popularity-Bias and Improving Diversity Through Calibrated Recommendations
SN - 978-989-758-648-4
AU - Sacilotti A.
AU - Souza R.
AU - G. Manzato M.
PY - 2023
SP - 709
EP - 720
DO - 10.5220/0011846000003467
PB - SciTePress