Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendations

TL;DR

This paper introduces a fairness-aware re-ranking method for recommendations that balances relevance and demographic fairness using image representations derived from labeled data, improving fairness with minimal data while maintaining high precision.

Contribution

It proposes a novel fairness-aware variation of the MMR re-ranking method that incorporates demographic group representations to enhance fairness in recommendations.

Findings

The method improves fairness in ranked results.

It achieves higher precision than the baseline MMR.

Limited labeled data suffices for effective fairness representation.

Abstract

The trade-off between relevance and fairness in personalized recommendations has been explored in recent works, with the goal of minimizing learned discrimination towards certain demographics while still producing relevant results. We present a fairness-aware variation of the Maximal Marginal Relevance (MMR) re-ranking method which uses representations of demographic groups computed using a labeled dataset. This method is intended to incorporate fairness with respect to these demographic groups. We perform an experiment on a stock photo dataset and examine the trade-off between relevance and fairness against a well known baseline, MMR, by using human judgment to examine the results of the re-ranking when using different fractions of a labeled dataset, and by performing a quantitative analysis on the ranked results of a set of query images. We show that our proposed method can incorporate fairness in the ranked results while obtaining higher precision than the baseline, while our case study shows that even a limited amount of labeled data can be used to compute the representations to obtain fairness. This method can be used as a post-processing step for recommender systems and search.