Multi-sided Exposure Bias in Recommendation

The Pareto principle: its use and abuse

Robert Sanders. 1987 · 1987

Earlier work this paper cites.

Item-Based Top-N Recommendation Algorithms

M. Deshpande and G. Karypis. 2004 · 2004

Earlier work this paper cites.

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

Chris Anderson. 2006 · 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.

Matrix factorization techniques for recommender systems

Y. Koren, R. Bell, and C. Volinsky. 2009 · 2009

Earlier work this paper cites.

Improving aggregate recommendation diversity using ranking-based techniques

Gediminas Adomavicius and YoungOk Kwon. 2011 · 2011

Earlier work this paper cites.

Rank and Relevance in Novelty and Diversity Metrics for Recommender Systems. In Proceedings of the Fifth ACM Conference on Recommender Systems (Chicago, Illinois, USA) (RecSys ’11) . ACM, New York, NY, USA, 109–116

Saúl Vargas and Pablo Castells. 2011 · 2011

Earlier work this paper cites.

Exploiting the diversity of user preferences for recommendation. In Proceedings of the 10th conference on open research areas in information retrieval . 129–136

Original

Saúl Vargas and Pablo Castells. 2013 · 2013

Earlier work this paper cites.

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.

Multi-sided platforms

Andrei Hagiu and Julian Wright. 2015 · 2015

Cited alongside, same era.

Neighborhood-based collaborative filtering

Charu C Aggarwal. 2016 · 2016

Cited alongside, same era.

The lfm-1b dataset for music retrieval and recommendation. In Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval . 103–110

Markus Schedl. 2016 · 2016

Cited alongside, same era.

Deconvolving feedback loops in recommender systems. In Advances in neural information processing systems . 3243–3251

Ayan Sinha, David F. Gleich, and Karthik Ramani. 2016 · 2016

Cited alongside, same era.

Controlling Popularity Bias in Learning to Rank Recommendation. In Proceedings of the 11th ACM conference on Recommender systems . ACM, 42–46

Towards a fair marketplace: Counterfactual evaluation of the trade-off between relevance, fairness & satisfaction in recommendation systems. In Proceedings of the 27th acm international conference on information and knowledge management . 2243–2251

Rishabh Mehrotra, James McInerney, Hugues Bouchard, Mounia Lalmas, and Fernando Diaz. 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.

The unfairness of popularity bias in recommendation. In RecSys Workshop on Recommendation in Multistakeholder Environments (RMSE)

Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher. 2019 · 2019

Later among the works it cites.

The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study

Original

Kowald Dominik, Schedl Markus, and Lex Elisabeth. 2019 · 2019

Later among the works it cites.