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

By Chen Karako, Putra Manggala. 2018

57 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

FairSight: Visual Analytics for Fairness in Decision Making (2019, methodology): “Recently, fair ranking methods have been proposed [2, 41, 23].”
Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models (2022, background): “However, it could also be applied to cases beyond inappropriateness, such as fairness [22].”
Multistakeholder recommendation: Survey and research directions (2020, methodology): “Tensor factorization allows content information and preference information to be combined in a single optimization (Zhu et al. 2018), and there has been research applying fairness in a separate re-ranking step including (Liu et al. 2019; Karako and Manggala 2018a).”
A Survey on the Fairness of Recommender Systems (2022, methodology): “Grouped by Concept Consistent Fairness CO similar individuals / different groups should receive similar outcomes [30, 36, 39, 48, 53, 54, 91, 93, 95, 97] [6, 28, 45, 68, 73, 86, 90, 107, 112, 116] [12, 22, 25, 43, 44, 52, 64, 85, 104, 114] [8, 20, 58, 60, 61, 75, 76, 78, 83, 94, 102] [82, 99, 106]”