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Mitigating sensitive data exposure with adversarial learning for fairness recommendation systems

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Abstract

Fairness is an important research problem for recommendation systems, and unfair recommendation methods can lead to discrimination against users. Gender is a kind of sensitive feature, exposure sensitive feature can lead to unfair treatment of males and females. However, gender discrimination is still a significant challenge that cannot be addressed well with current recommender methods. To alleviate this problem, we present a novel Unbias Gender Recommendation (UGRec) to balance performance between females and males. We propose a multihop graph aggregation mechanism to improve the representation of users and items, and we design a gender debias component with adversarial learning to eliminate gender bias by filtering out users’ representations. With this design, UGRec can effectively filter out gender bias information and balance fairness between females and males. The experimental results based on three datasets demonstrate the effectiveness and advancement of our proposed UGRec model on fair recommendation.

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Acknowledgements

This work is partially supported by Grant from the Natural Science Foundation of China (Nos. 61772103, 62076046, 62006034), the Ministry of Education Humanities and Social Science Project (No. 19YJC ZH199).

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Authors and Affiliations

  1. School of Computer Science and Technology, Dalian University of Technology, Dalian, China

    Haifeng Liu, Hongfei Lin, Bo Xu & Nan Zhao

  2. Faculty of Humanities and Social Sciences, Dalian University of Technology, Dalian, China

    Yukai Wang

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  1. Haifeng Liu
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Correspondence to Hongfei Lin.

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Liu, H., Wang, Y., Lin, H. et al. Mitigating sensitive data exposure with adversarial learning for fairness recommendation systems. Neural Comput & Applic 34, 18097–18111 (2022). https://doi.org/10.1007/s00521-022-07373-4

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