Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendations
This work addresses fairness in recommendations for users, but it is incremental as it builds on existing re-ranking methods with a specific fairness adaptation.
The authors tackled the trade-off between relevance and fairness in personalized recommendations by proposing a fairness-aware variation of the Maximal Marginal Relevance re-ranking method, showing it can incorporate fairness while achieving higher precision than the baseline, with a case study indicating that limited labeled data suffices for this purpose.
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.