Debiasing Career Recommendations with Neural Fair Collaborative Filtering

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Islam (2021) - Debiasing Career Recommendations with Neural Fair Collaborative Filtering (WWW).pdf (1.16 MB)

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http://jfoulds.informationsystems.umbc.edu/papers/2021/Islam%20(2021)%20-%20Debiasing%20Career%20Recommendations%20with%20Neural%20Fair%20Collaborative%20Filtering%20(WWW).pdf

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https://doi.org/10.1145/3442381.3449904
 http://hdl.handle.net/11603/21218

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UMBC Information Systems Department
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conference papers and proceedings
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Rashidul Islam, Kamrun Naher Keya, Ziqian Zeng, Shimei Pan, and James Foulds. 2021. Debiasing Career Recommendations with Neural Fair Collaborative Filtering. In Proceedings of the Web Conference 2021 (WWW ’21), April 19–23, 2021, Ljubljana, Slovenia. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3442381.3449904

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Abstract

A growing proportion of human interactions are digitized on social media platforms and subjected to algorithmic decision-making, and it has become increasingly important to ensure fair treatment from these algorithms. In this work, we investigate gender bias in collaborative-filtering recommender systems trained on social media data. We develop neural fair collaborative filtering (NFCF), a practical framework for mitigating gender bias in recommending career-related sensitive items (e.g. jobs, academic concentrations, or courses of study) using a pre-training and fine-tuning approach to neural collaborative filtering, augmented with bias correction techniques. We show the utility of our methods for gender de-biased career and college major recommendations on the MovieLens dataset and a Facebook dataset, respectively, and achieve better performance and fairer behavior than several state-of-the-art models.