FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data

TL;DR

FairRoad introduces a novel method to enhance fairness in recommender systems by creating a small, optimized antidote dataset through mathematical optimization, effectively reducing bias without altering existing algorithms.

Contribution

The paper presents a new antidote data generation approach, formulated as an optimization problem, to improve fairness in recommender systems without modifying their algorithms.

Findings

Significantly improves fairness with minimal antidote data

Outperforms existing fairness enhancement methods

Maintains recommendation accuracy while reducing bias

Abstract

Today, recommender systems have played an increasingly important role in shaping our experiences of digital environments and social interactions. However, as recommender systems become ubiquitous in our society, recent years have also witnessed significant fairness concerns for recommender systems. Specifically, studies have shown that recommender systems may inherit or even amplify biases from historical data, and as a result, provide unfair recommendations. To address fairness risks in recommender systems, most of the previous approaches to date are focused on modifying either the existing training data samples or the deployed recommender algorithms, but unfortunately with limited degrees of success. In this paper, we propose a new approach called fair recommendation with optimized antidote data (FairRoad), which aims to improve the fairness performances of recommender systems through the construction of a small and carefully crafted antidote dataset. Toward this end, we formulate our antidote data generation task as a mathematical optimization problem, which minimizes the unfairness of the targeted recommender systems while not disrupting the deployed recommendation algorithms. Extensive experiments show that our proposed antidote data generation algorithm significantly improve the fairness of recommender systems with a small amounts of antidote data.