FairBayRank: A Fair Personalized Bayesian Ranker

Armielle Noulapeu Ngaffo; Julien Albert; Benoît Frénay; Gilles Perrouin

FairBayRank: A Fair Personalized Bayesian Ranker

Armielle Noulapeu Ngaffo, Julien Albert, Benoît Frénay, Gilles Perrouin

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Résumé

Recommender systems are data-driven models that successfully pro- vide users with personalized rankings of items (movies, books...). Meanwhile, for user minority groups, those systems can be unfair in predicting users’ expectations due to biased data. Consequently, fairness remains an open challenge in the rank- ing prediction task. To address this issue, we propose in this paper FairBayRank, a fair Bayesian personalized ranking algorithm that deals with both fairness and ranking performance requirements. FairBayRank evaluation on real-world datasets shows that it efficiently alleviates unfairness issues while ensuring high prediction performances.

langue originale	Anglais
titre	31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning Bruges, Belgium October 04 - 06
Etat de la publication	Publié - 2023
Evénement	31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Bruges, Belgique Durée: 4 oct. 2023 → 6 oct. 2023 Numéro de conférence: 31 https://www.esann.org

Une conférence

Une conférence	31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Pays/Territoire	Belgique
La ville	Bruges
période	4/10/23 → 6/10/23
Adresse Internet	https://www.esann.org

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ES2023-81License: autre

https://www.esann.org/sites/default/files/proceedings/2023/ES2023-81.pdfLicense: autre

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@inproceedings{f422027f152a43e795509a5ea19193a6,

title = "FairBayRank: A Fair Personalized Bayesian Ranker",

abstract = "Recommender systems are data-driven models that successfully pro- vide users with personalized rankings of items (movies, books...). Meanwhile, for user minority groups, those systems can be unfair in predicting users{\textquoteright} expectations due to biased data. Consequently, fairness remains an open challenge in the rank- ing prediction task. To address this issue, we propose in this paper FairBayRank, a fair Bayesian personalized ranking algorithm that deals with both fairness and ranking performance requirements. FairBayRank evaluation on real-world datasets shows that it efficiently alleviates unfairness issues while ensuring high prediction performances.",

author = "{Noulapeu Ngaffo}, Armielle and Julien Albert and Beno{\^i}t Fr{\'e}nay and Gilles Perrouin",

year = "2023",

language = "English",

booktitle = "31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning Bruges, Belgium October 04 - 06",

note = "31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning ; Conference date: 04-10-2023 Through 06-10-2023",

url = "https://www.esann.org",

}

Noulapeu Ngaffo, A , Albert, J , Frénay, B & Perrouin, G 2023, FairBayRank: A Fair Personalized Bayesian Ranker. Dans 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning Bruges, Belgium October 04 - 06. 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgique, 4/10/23. <https://www.esann.org/sites/default/files/proceedings/2023/ES2023-81.pdf>

FairBayRank: A Fair Personalized Bayesian Ranker. / Noulapeu Ngaffo, Armielle ; Albert, Julien ; Frénay, Benoît et al.
31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning Bruges, Belgium October 04 - 06. 2023.

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférence › Article dans les actes d'une conférence/un colloque

TY - GEN

T1 - FairBayRank

T2 - 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

AU - Noulapeu Ngaffo, Armielle

AU - Albert, Julien

AU - Frénay, Benoît

AU - Perrouin, Gilles

N1 - Conference code: 31

PY - 2023

Y1 - 2023

N2 - Recommender systems are data-driven models that successfully pro- vide users with personalized rankings of items (movies, books...). Meanwhile, for user minority groups, those systems can be unfair in predicting users’ expectations due to biased data. Consequently, fairness remains an open challenge in the rank- ing prediction task. To address this issue, we propose in this paper FairBayRank, a fair Bayesian personalized ranking algorithm that deals with both fairness and ranking performance requirements. FairBayRank evaluation on real-world datasets shows that it efficiently alleviates unfairness issues while ensuring high prediction performances.

AB - Recommender systems are data-driven models that successfully pro- vide users with personalized rankings of items (movies, books...). Meanwhile, for user minority groups, those systems can be unfair in predicting users’ expectations due to biased data. Consequently, fairness remains an open challenge in the rank- ing prediction task. To address this issue, we propose in this paper FairBayRank, a fair Bayesian personalized ranking algorithm that deals with both fairness and ranking performance requirements. FairBayRank evaluation on real-world datasets shows that it efficiently alleviates unfairness issues while ensuring high prediction performances.

M3 - Conference contribution

BT - 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning Bruges, Belgium October 04 - 06

Y2 - 4 October 2023 through 6 October 2023

ER -

FairBayRank: A Fair Personalized Bayesian Ranker

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