research-article

Enhancing Fairness in Meta-learned User Modeling via Adaptive Sampling

Authors:

Qingyang MaoAuthors Info & Claims

WWW '24: Proceedings of the ACM Web Conference 2024

Pages 3241 - 3252

https://doi.org/10.1145/3589334.3645369

Published: 13 May 2024 Publication History

Abstract

Meta-learning has been widely employed to tackle the cold-start problem in user modeling. Similar to a guidebook for a new traveler, meta-learning significantly affects decision-making for new users in crucial scenarios, such as career recommendations. Consequently, the issue of fairness in meta-learning has gained paramount importance. Several methods have been proposed to mitigate unfairness in meta-learning and have shown promising results. However, a fundamental question remains unexplored: What is the critical factor leading to unfairness in meta-learned user modeling? Through the theoretical analysis that integrates the meta-learning paradigm with group fairness metrics, we identify group proportion imbalance as a critical factor. Subsequently, in order to mitigate the impact of this factor, we introduce a novel Fairness-aware Adaptive Sampling framework for meTa-learning, abbreviated as FAST. Its core concept involves adaptively adjusting the sampling distribution for different user groups during the interleaved training process of meta-learning. Furthermore, we provide theoretical guarantees demonstrating the convergence of FAST. Finally, empirical experiments conducted on three datasets reveal that FAST effectively enhances fairness while maintaining high accuracy. The code for FAST is available at https://github.com/zhengz99/FAST.

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Cited By

Hu ZZhang ZFeng WLiu Q(2024)Achieving Universal Fairness in Machine Learning: A Multi-objective Optimization PerspectiveKnowledge Science, Engineering and Management10.1007/978-981-97-5495-3_12(164-179)Online publication date: 26-Jul-2024
https://doi.org/10.1007/978-981-97-5495-3_12

Index Terms

Enhancing Fairness in Meta-learned User Modeling via Adaptive Sampling
1. Human-centered computing

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Published In

cover image ACM Conferences

WWW '24: Proceedings of the ACM Web Conference 2024

May 2024

4826 pages

ISBN:9798400701719

DOI:10.1145/3589334

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Chong-Wah Ngo
Singapore Management University
,
Proceedings Chair:
Roy Ka-Wei Lee
Singapore University of Technology and Design
,
Program Chairs:
Ravi Kumar
Google
,
Hady W. Lauw
Singapore Management University

Copyright © 2024 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 13 May 2024

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the National Key Research and Development Program of China
the Anhui Provincial Natural Science Foundation

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WWW '24

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WWW '24: The ACM Web Conference 2024

May 13 - 17, 2024

Singapore, Singapore

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Cited By

Hu ZZhang ZFeng WLiu Q(2024)Achieving Universal Fairness in Machine Learning: A Multi-objective Optimization PerspectiveKnowledge Science, Engineering and Management10.1007/978-981-97-5495-3_12(164-179)Online publication date: 26-Jul-2024
https://doi.org/10.1007/978-981-97-5495-3_12

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