Neural Fair Collaborative Filtering
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.
Cite
@article{arxiv.2009.08955,
title = {Neural Fair Collaborative Filtering},
author = {Rashidul Islam and Kamrun Naher Keya and Ziqian Zeng and Shimei Pan and James Foulds},
journal= {arXiv preprint arXiv:2009.08955},
year = {2020}
}