The Unfairness of Popularity Bias in Recommendation

The long tail: Why the future of business is selling more for less

Chris Anderson. 2006 · 2006

Earlier work this paper cites.

From niches to riches: Anatomy of the long tail

Erik Brynjolfsson, Yu Jeffrey Hu, and Michael D Smith. 2006a · 2006

Earlier work this paper cites.

From niches to riches: Anatomy of the long tail

Erik Brynjolfsson, Yu Jeffrey Hu, and Michael D Smith. 2006b · 2006

Earlier work this paper cites.

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 . ACM, 5

Òscar Celma and Pedro Cano. 2008 · 2008

Earlier work this paper cites.

The long tail of recommender systems and how to leverage it. In Proceedings of the 2008 ACM conference on Recommender systems . ACM, 11–18

Yoon-Joo Park and Alexander Tuzhilin. 2008 · 2008

Earlier work this paper cites.

Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques

Gediminas Adomavicius and YoungOk Kwon. 2012 · 2011

Earlier work this paper cites.

Fairness-aware Classifier with Prejudice Remover Regularizer. In Proc. of the ECML PKDD 2012, Part II . 35–50

Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma. 2012 · 2012

Earlier work this paper cites.

Bursting your (filter) bubble: strategies for promoting diverse exposure. In Proceedings of the 2013 conference on Computer supported cooperative work companion . ACM, 95–100

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

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 . ACM, 677–686

Tien T Nguyen, Pik-Mai Hui, F Maxwell Harper, Loren Terveen, and Joseph A Konstan. 2014 · 2014

Cited alongside, same era.

The MovieLens Datasets: History and Context

F Maxwell Harper and Joseph A Konstan. 2015 · 2015

Cited alongside, same era.

Beyond Parity: Fairness Objectives for Collaborative Filtering

Original

Sirui Yao and Bert Huang. 2017 · 2017

Later among the works it cites.

Personalizing Fairness-aware Re-ranking

Original

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 . ACM, 154–162

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 . ACM, 1153–1162

Ziwei Zhu, Xia Hu, and James Caverlee. 2018 · 2018

Later among the works it cites.

Popularity Bias in Ranking and Recommendation. In In AAAI/ACM Conference on AI, Ethics, and Society (AIES’19) January 27–28, 2019, Honolulu, HI, USA. ACM