research-article

FairGAN: GANs-based Fairness-aware Learning for Recommendations with Implicit Feedback

Authors:

Ke DengAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 297 - 307

https://doi.org/10.1145/3485447.3511958

Published: 25 April 2022 Publication History

Abstract

Ranking algorithms in recommender systems influence people to make decisions. Conventional ranking algorithms based on implicit feedback data aim to maximize the utility to users by capturing users’ preferences over items. However, these utility-focused algorithms tend to cause fairness issues that require careful consideration in online platforms. Existing fairness-focused studies does not explicitly consider the problem of lacking negative feedback in implicit feedback data, while previous utility-focused methods ignore the importance of fairness in recommendations. To fill this gap, we propose a Generative Adversarial Networks (GANs) based learning algorithm FairGAN mapping the exposure fairness issue to the problem of negative preferences in implicit feedback data. FairGAN does not explicitly treat unobserved interactions as negative, but instead, adopts a novel fairness-aware learning strategy to dynamically generate fairness signals. This optimizes the search direction to make FairGAN capable of searching the space of the optimal ranking that can fairly allocate exposure to individual items while preserving users’ utilities as high as possible.

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FairGAN: GANs-based Fairness-aware Learning for Recommendations with Implicit Feedback

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cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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Cited By

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https://doi.org/10.12677/mos.2024.133320
Zhang KQian XSong W(2024)GAN-based Fairness-Aware Recommendation for Enhancing the Fairness of DataGAN-based Fairness-Aware RecommendationFairness-Aware RecommendationProceedings of the 2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence10.1145/3675417.3675468(315-321)Online publication date: 19-Jan-2024
https://dl.acm.org/doi/10.1145/3675417.3675468
Yang HWu XQiu ZZheng YChen X(2024)Distributional Fairness-aware RecommendationACM Transactions on Information Systems10.1145/365285442:5(1-28)Online publication date: 29-Apr-2024
https://dl.acm.org/doi/10.1145/3652854
Wyllie SShumailov IPapernot N(2024)Fairness Feedback Loops: Training on Synthetic Data Amplifies BiasProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3659029(2113-2147)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3659029
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