Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendations. Karako, C. & Manggala, P. In Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization, of UMAP '18, pages 23–28. ACM. event-place: Singapore, Singapore
Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendations [link]Paper  doi  abstract   bibtex   
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.
@inproceedings{karako_using_2018,
	location = {New York, {NY}, {USA}},
	title = {Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendations},
	isbn = {978-1-4503-5784-5},
	url = {http://doi.acm.org/10.1145/3213586.3226206},
	doi = {10.1145/3213586.3226206},
	series = {{UMAP} '18},
	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.},
	pages = {23--28},
	booktitle = {Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization},
	publisher = {{ACM}},
	author = {Karako, Chen and Manggala, Putra},
	urldate = {2019-07-10},
	date = {2018},
	note = {event-place: Singapore, Singapore},
	keywords = {fairness, recommender systems, diversity, diversity, fairness, information retrieval, recommender systems, fatrec, information retrieval}
}
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