Mitigating Popularity Bias for Users and Items with Fairness-centric Adaptive Recommendation

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Mitigating Popularity Bias for Users and Items with Fairness-centric Adaptive Recommendation
Title:
Mitigating Popularity Bias for Users and Items with Fairness-centric Adaptive Recommendation
Journal Title:
ACM Transactions on Information Systems
Publication Date:
19 September 2022
Citation:
Liu, Z., Fang, Y., & Wu, M. (2022). Mitigating Popularity Bias for Users and Items with Fairness-centric Adaptive Recommendation. ACM Transactions on Information Systems. https://doi.org/10.1145/3564286
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 is 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 f airness-centric model that a dapt i vely mitigates the popularity bias in both users and items for r ecommendation. 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.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - AME Programmatic Funds
Grant Reference no. : A20H6b0151
Description:
© { Author | ACM 2022. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Information Systems, http://dx.doi.org/10.1145/3564286
ISSN:
1558-2868
1046-8188
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