Neural Fair Collaborative Filtering

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2009.08955v1.pdf (1.06 MB)

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https://arxiv.org/abs/2009.08955

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http://hdl.handle.net/11603/20624

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UMBC Information Systems Department
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journal articles preprints
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Citation of Original Publication

Rashidul Islam, Kamrun Naher Keya, Ziqian Zeng, Shimei Pan and James Foulds, Neural Fair Collaborative Filtering, https://arxiv.org/abs/2009.08955

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Subjects

information retrieval
machine learning
algorithms
automate decisions

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 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.