Bias Disparity in Recommendation Systems

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 (NETFLIX ’08) . ACM, 5:1–5:8

Òscar Celma and Pedro Cano. 2008 · 2008

Earlier work this paper cites.

A Survey of Collaborative Filtering Techniques

Xiaoyuan Su and Taghi M. Khoshgoftaar. 2009 · 2009

Earlier work this paper cites.

SLIM: Sparse Linear Methods for Top-N Recommender Systems. In Proceedings of the 2011 IEEE 11th International Conference on Data Mining (ICDM ’11) . IEEE Computer Society, 497–506

Xia Ning and George Karypis. 2011 · 2011

Earlier work this paper cites.

Fairness Through Awareness. In Proceedings of the 3rd Innovations in Theoretical Computer Science Conference (ITCS ’12) . ACM, 214–226

Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. 2012 · 2012

Earlier work this paper cites.

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.

Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining. In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’16) . ACM, 2125–2126

Sara Hajian, Francesco Bonchi, and Carlos Castillo. 2016 · 2016

Cited alongside, same era.

Diversity in Recommender Systems A Survey

Matev Kunaver and Toma Porl. 2017 · 2017

Cited alongside, same era.

On Measuring Bias in Online Information

Original

Evaggelia Pitoura, Panayiotis Tsaparas, Giorgos Flouris, Irini Fundulaki, Panagiotis Papadakos, Serge Abiteboul, and Gerhard Weikum. 2017 · 2017

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.

Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints

Original

Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2017 · 2017

Later among the works it cites.

Balanced Neighborhoods for Multi-sided Fairness in Recommendation. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (Proceedings of Machine Learning Research) , Sorelle A. Friedler and Christo Wilson (Eds.), Vol. 81. PMLR

Robin Burke, Nasim Sonboli, and Aldo Ordonez-Gauger. 2018 · 2018

Closest in time.