Publication Type

Journal Article

Version

publishedVersion

Publication Date

2-2023

Abstract

Recommendation systems are popular in many domains. Researchers usually focus on the effectiveness of recommendation (e.g., precision) but neglect the popularity bias that may affect the fairness of the recommendation, which is also an important consideration that could influence the benefits of users and item providers. A few studies have been proposed to deal with the popularity bias, but they often face two limitations. Firstly, most studies only consider fairness for one side - either users or items, without achieving fairness jointly for both. Secondly, existing methods are not sufficiently tailored to each individual user or item to cope with the varying extent and nature of popularity bias. To alleviate these limitations, in this paper, we propose FAiR, a fairness-centric model that adaptively mitigates the popularity bias in both users and items for recommendation. Concretely, we design explicit fairness discriminators to mitigate the popularity bias for each user and item locally, and an implicit discriminator to preserve fairness globally. Moreover, we dynamically adapt the model to different input users and items to handle the differences in their popularity bias. Finally, we conduct extensive experiments to demonstrate that our model significantly outperforms state-of-the-art baselines in fairness metrics, while remaining competitive in effectiveness.

Keywords

Fairness, popularity bias

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

ACM Transactions on Information Systems

Volume

41

Issue

3

First Page

1

Last Page

27

ISSN

1046-8188

Identifier

10.1145/3564286

Publisher

ACM

Embargo Period

9-12-2023

Copyright Owner and License

Authors

Creative Commons License

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

Additional URL

https://doi.org/10.1145/3564286

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