Neural Fair Collaborative Filtering

TL;DR

This paper introduces Neural Fair Collaborative Filtering (NFCF), a framework designed to reduce gender bias in social media recommender systems by combining pre-training, fine-tuning, and bias correction, improving fairness and accuracy.

Contribution

The paper presents NFCF, a novel neural collaborative filtering approach that effectively mitigates gender bias in recommendations for sensitive items, outperforming existing models.

Findings

NFCF reduces gender bias in career and major recommendations.

NFCF achieves better performance than state-of-the-art models.

The method is validated on MovieLens and Facebook datasets.

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.