Fighting Fire with Fire: Using Antidote Data to Improve Polarization and Fairness of Recommender Systems

TL;DR

This paper proposes adding carefully crafted antidote data to recommender systems to enhance social desirability by reducing polarization and improving fairness, without extensive modifications to existing algorithms.

Contribution

It formalizes the antidote data problem, develops optimization solutions, and demonstrates effectiveness in improving fairness and reducing polarization in matrix factorization recommenders.

Findings

Modest antidote data can significantly reduce recommendation polarization.

Antidote data improves fairness with minimal impact on accuracy.

Optimization methods efficiently generate antidote data for social measures.

Abstract

The increasing role of recommender systems in many aspects of society makes it essential to consider how such systems may impact social good. Various modifications to recommendation algorithms have been proposed to improve their performance for specific socially relevant measures. However, previous proposals are often not easily adapted to different measures, and they generally require the ability to modify either existing system inputs, the system's algorithm, or the system's outputs. As an alternative, in this paper we introduce the idea of improving the social desirability of recommender system outputs by adding more data to the input, an approach we view as providing `antidote' data to the system. We formalize the antidote data problem, and develop optimization-based solutions. We take as our model system the matrix factorization approach to recommendation, and we propose a set of measures to capture the polarization or fairness of recommendations. We then show how to generate antidote data for each measure, pointing out a number of computational efficiencies, and discuss the impact on overall system accuracy. Our experiments show that a modest budget for antidote data can lead to significant improvements in the polarization or fairness of recommendations.