Neural Fair Collaborative Filtering
This addresses fairness issues in algorithmic recommendations for sensitive items like jobs and academic majors, though it is incremental as it builds on existing neural collaborative filtering methods.
The paper tackled gender bias in collaborative-filtering recommender systems on social media data by developing Neural Fair Collaborative Filtering (NFCF), a framework using pre-training, fine-tuning, and bias correction, which achieved better performance and fairness than state-of-the-art models on MovieLens and Facebook datasets.
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.