DeepFair: Deep Learning for Improving Fairness in Recommender Systems

TL;DR

DeepFair introduces a deep learning-based collaborative filtering method that enhances fairness in recommender systems without requiring demographic data, balancing equity and accuracy effectively.

Contribution

It presents a novel deep learning approach that improves fairness in recommendations without sacrificing significant accuracy, addressing bias without demographic info.

Findings

Fair recommendations achieved without demographic data

Balance between fairness and accuracy demonstrated

Method outperforms traditional approaches in fairness metrics

Abstract

The lack of bias management in Recommender Systems leads to minority groups receiving unfair recommendations. Moreover, the trade-off between equity and precision makes it difficult to obtain recommendations that meet both criteria. Here we propose a Deep Learning based Collaborative Filtering algorithm that provides recommendations with an optimum balance between fairness and accuracy without knowing demographic information about the users. Experimental results show that it is possible to make fair recommendations without losing a significant proportion of accuracy.