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Recently there has been a growing interest in fairness-aware recommender systems, including fairness in providing consistent performance across different users or groups of users.
Recommender systems
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From hits to niches?: or how popular artists can bias music recommendation and discovery. In Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition
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Yoon-Joo Park and Alexander Tuzhilin. 2008 · 2008
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Matrix factorization techniques for recommender systems
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Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques
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Novel recommendation based on personal popularity tendency. In 2011 IEEE 11th International Conference on Data Mining
Jinoh Oh, Sun Park, Hwanjo Yu, Min Song, and Seung-Taek Park. 2011 · 2011
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Hellinger distance decision trees are robust and skew-insensitive
David A Cieslak, T Ryan Hoens, Nitesh V Chawla, and W Philip Kegelmeyer. 2012 · 2012
Cited alongside, same era.
Fairness-aware Classifier with Prejudice Remover Regularizer. In Proc. of the ECML PKDD 2012, Part II
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma. 2012 · 2012
Cited alongside, same era.
Bursting your (filter) bubble: strategies for promoting diverse exposure. In Proceedings of the 2013 conference on Computer supported cooperative work companion
Paul Resnick, R Kelly Garrett, Travis Kriplean, Sean A Munson, and Natalie Jomini Stroud. 2013 · 2013
Cited alongside, same era.
Correcting Popularity Bias by Enhancing Recommendation Neutrality. In Poster Proceedings of the 8th ACM Conference on Recommender Systems, RecSys 2014, Foster City, Silicon Valley, CA, USA, October 6-10, 2014
Controlling Popularity Bias in Learning to Rank Recommendation. In Proceedings of the 11th ACM conference on Recommender systems
Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2017 · 2017
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Balanced Neighborhoods for Fairness-aware Collaborative Recommendation. In Workshop on Responsible Recommendation (FATRec)
Robin Burke, Nasim Sonboli, Masoud Mansoury, and Aldo Ordoñez-Gauger. 2017 · 2017
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Beyond Parity: Fairness Objectives for Collaborative Filtering
Sirui Yao and Bert Huang. 2017 · 2017
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All The Cool Kids, How Do They Fit In?: Popularity and Demographic Biases in Recommender Evaluation and Effectiveness. In Conference on Fairness, Accountability and Transparency
Michael D Ekstrand, Mucun Tian, Ion Madrazo Azpiazu, Jennifer D Ekstrand, Oghenemaro Anuyah, David McNeill, and Maria Soledad Pera. 2018 · 2018
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Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma. 2014 · 2014
Cited alongside, same era.
Exploring the filter bubble: the effect of using recommender systems on content diversity. In Proceedings of the 23rd international conference on World wide web
Tien T Nguyen, Pik-Mai Hui, F Maxwell Harper, Loren Terveen, and Joseph A Konstan. 2014 · 2014
Cited alongside, same era.
LibRec: A Java Library for Recommender Systems.. In UMAP Workshops
Guibing Guo, Jie Zhang, Zhu Sun, and Neil Yorke-Smith. 2015 · 2015
Cited alongside, same era.
The MovieLens Datasets: History and Context
F Maxwell Harper and Joseph A Konstan. 2015 · 2015
Cited alongside, same era.
What recommenders recommend: an analysis of recommendation biases and possible countermeasures
Dietmar Jannach, Lukas Lerche, Iman Kamehkhosh, and Michael Jugovac. 2015 · 2015
Cited alongside, same era.
Managing Popularity Bias in Recommender Systems with Personalized Re-ranking.. In Florida AI Research Symposium (FLAIRS)
Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2019b
Cited in the paper.
Weiwen Liu and Robin Burke. 2018 · 2018
Later among the works it cites.
Calibrated recommendations. In Proceedings of the 12th ACM Conference on Recommender Systems
Harald Steck. 2018 · 2018
Later among the works it cites.
Fairness-Aware Tensor-Based Recommendation. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management
Ziwei Zhu, Xia Hu, and James Caverlee. 2018 · 2018
Later among the works it cites.
Beyond Personalization: Research Directions in Multistakeholder Recommendation
Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, and Luiz Pizzato. 2019a · 2019
Closest in time.
The Unfairness of Popularity Bias in Recommendation
Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher. 2019c · 2019
Closest in time.