Dual-Side Adversarial Learning Based Fair Recommendation for Sensitive Attribute Filtering
Authors: 
Shenghao Liu, Yu Zhang, Lingzhi Yi, Xianjun Deng, Laurence T. Yang, Bang WangAuthors Info & Claims
Shenghao Liu, Yu Zhang, Lingzhi Yi, Xianjun Deng, Laurence T. Yang, Bang WangAuthors Info & ClaimsArticle No.: 165, Pages 1 - 20
Abstract
With the development of recommendation algorithms, researchers are paying increasing attention to fairness issues such as user discrimination in recommendations. To address these issues, existing works often filter users’ sensitive information that may cause discrimination during the process of learning user representations. However, these approaches overlook the latent relationship between items’ content attributes and users’ sensitive information. In this article, we propose DALFRec, a fairness-aware recommendation algorithm based on user-side and item-side adversarial learning to mitigate the effects of sensitive information on both sides of the recommendation process. First, we conduct a statistical analysis to demonstrate the latent relationship between items’ information and users’ sensitive attributes. Then, we design a dual-side adversarial learning network that simultaneously filters out users’ sensitive information on the user and item side. Additionally, we propose a new evaluation strategy that leverages the latent relationship between items’ content attributes and users’ sensitive attributes to better assess the algorithm’s ability to reduce discrimination. Our experiments on three real datasets demonstrate the superiority of our proposed algorithm over state-of-the-art methods.
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- Dual-Side Adversarial Learning Based Fair Recommendation for Sensitive Attribute Filtering
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Publication History
Published: 19 June 2024
Online AM: 19 February 2024
Accepted: 14 February 2024
Revised: 26 December 2023
Received: 21 August 2023
Published in TKDD Volume 18, Issue 7
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