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Authors: Valentijn Braun ; Debarati Bhaumik and Diptish Dey

Affiliation: Amsterdam University of Applied Sciences, Netherlands

Keyword(s): Recommender Systems, Popularity Bias, Fairness, Dynamic.

Abstract: Albeit the widespread application of recommender systems (RecSys) in our daily lives, rather limited research has been done on quantifying unfairness and biases present in such systems. Prior work largely focuses on determining whether a RecSys is discriminating or not but does not compute the amount of bias present in these systems. Biased recommendations may lead to decisions that can potentially have adverse effects on individuals, sensitive user groups, and society. Hence, it is important to quantify these biases for fair and safe commercial applications of these systems. This paper focuses on quantifying popularity bias that stems directly from the output of RecSys models, leading to over recommendation of popular items that are likely to be misaligned with user preferences. Four metrics to quantify popularity bias in RescSys over time in dynamic setting across different sensitive user groups have been proposed. These metrics have been demonstrated for four collaborative filteri ng based RecSys algorithms trained on two commonly used benchmark datasets in the literature. Results obtained show that the metrics proposed provide a comprehensive understanding of growing disparities in treatment between sensitive groups over time when used conjointly. (More)

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Paper citation in several formats:
Braun, V.; Bhaumik, D. and Dey, D. (2024). Metrics for Popularity Bias in Dynamic Recommender Systems. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-680-4; ISSN 2184-433X, SciTePress, pages 121-134. DOI: 10.5220/0012316700003636

@conference{icaart24,
author={Valentijn Braun. and Debarati Bhaumik. and Diptish Dey.},
title={Metrics for Popularity Bias in Dynamic Recommender Systems},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2024},
pages={121-134},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012316700003636},
isbn={978-989-758-680-4},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Metrics for Popularity Bias in Dynamic Recommender Systems
SN - 978-989-758-680-4
IS - 2184-433X
AU - Braun, V.
AU - Bhaumik, D.
AU - Dey, D.
PY - 2024
SP - 121
EP - 134
DO - 10.5220/0012316700003636
PB - SciTePress

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