Chen Karako
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.
- {n.d.}. Burst. https://burst.shopify.com/. Accessed: 2018-04--18.Google Scholar
- Abolfazl Asudehy, HV Jagadishy, Julia Stoyanovichz, and Gautam Das. 2017. Designing Fair Ranking Schemes. arXiv preprint arXiv:1712.09752 (2017).Google Scholar
- Joy Buolamwini and Timnit Gebru. 2018. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on Fairness, Accountability and Transparency. 77--91.Google Scholar
- Robin Burke, Nasim Sonboli, Masoud Mansoury, and Aldo Ordoñez-Gauger. 2017. Balanced Neighborhoods for Fairness-aware Collaborative Recommendation. (2017).Google Scholar
- Jaime Carbonell and Jade Goldstein. 1998. The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 335--336. Google ScholarDigital Library
- L Elisa Celis, Damian Straszak, and Nisheeth K Vishnoi. 2017. Ranking with fairness constraints. arXiv preprint arXiv:1704.06840 (2017).Google Scholar
- Yoav Goldberg and Omer Levy. 2014. word2vec explained: Deriving mikolov et al.'s negative-sampling word-embedding method. arXiv preprint arXiv:1402.3722 (2014).Google Scholar
- Yushi Jing, David Liu, Dmitry Kislyuk, Andrew Zhai, Jiajing Xu, Jeff Donahue, and Sarah Tavel. 2015. Visual search at pinterest. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1889--1898. Google ScholarDigital Library
- Jeff Johnson, Matthijs Douze, and Hervé Jégou. 2017. Billion-scale similarity search with GPUs. arXiv preprint arXiv:1702.08734 (2017).Google Scholar
- Honglak Lee. 2010. Unsupervised feature learning via sparse hierarchical representations. Stanford University.Google Scholar
- Xia Ning and George Karypis. 2011. Slim: Sparse linear methods for top-n recommender systems. In Data Mining (ICDM), 2011 IEEE 11th International Conference on. IEEE, 497--506. Google ScholarDigital Library
- Zhou Ren, Hailin Jin, Zhe Lin, Chen Fang, and Alan Yuille. 2016. Joint image-text representation by gaussian visual-semantic embedding. In Proceedings of the 2016 ACM on Multimedia Conference. ACM, 207--211. Google ScholarDigital Library
- Scott Sanner, Shengbo Guo, Thore Graepel, Sadegh Kharazmi, and Sarvnaz Karimi. 2011. Diverse retrieval via greedy optimization of expected 1-call@ k in a latent subtopic relevance model. In Proceedings of the 20th ACM international conference on Information and knowledge management. ACM, 1977--1980. Google ScholarDigital Library
- Devashish Shankar, Sujay Narumanchi, HA Ananya, Pramod Kompalli, and Krishnendu Chaudhury. 2017. Deep learning based large scale visual recommendation and search for E-Commerce. arXiv preprint arXiv:1703.02344 (2017).Google Scholar
- Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).Google Scholar
- Ashudeep Singh and Thorsten Joachims. 2018. Fairness of Exposure in Rankings. arXiv preprint arXiv:1802.07281 (2018).Google Scholar
- Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2818--2826.Google Scholar
- Fan Yang, Ajinkya Kale, Yury Bubnov, Leon Stein, Qiaosong Wang, Hadi Kiapour, and Robinson Piramuthu. 2017. Visual search at ebay. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2101--2110. Google ScholarDigital Library
- Ke Yang and Julia Stoyanovich. 2016. Measuring fairness in ranked outputs. arXiv preprint arXiv:1610.08559 (2016).Google Scholar
- Jun Yu, Sunil Mohan, Duangmanee Pew Putthividhya, and Weng-Keen Wong. 2014. Latent dirichlet allocation based diversified retrieval for e-commerce search. In Proceedings of the 7th ACM international conference on Web search and data mining. ACM, 463--472. Google ScholarDigital Library
- Meike Zehlike, Francesco Bonchi, Carlos Castillo, Sara Hajian, Mohamed Megahed, and Ricardo Baeza-Yates. 2017. Fair: A fair top-k ranking algorithm. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 1569--1578. Google ScholarDigital Library
- Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork. 2013. Learning fair representations. In International Conference on Machine Learning. 325--333. Google ScholarDigital Library
Index Terms
- Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendations
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