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What we discover and see online, and consequently our opinions and decisions, are becoming increasingly affected by automated machine learned predictions.
Managing Popularity Bias in Recommender Systems with Personalized Re-ranking
Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2019 · 1901
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Degenerate Feedback Loops in Recommender Systems
Ray Jiang, Silvia Chiappa, Tor Lattimore, András György, and Pushmeet Kohli. 2019 · 1902
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Variabilità e mutabilità: contributo allo studio delle distribuzioni e delle relazioni statistiche. [|.]
C. Gini. 1912 · 1912
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The Multi-Armed Bandit Problem: Decomposition and Computation
Michael N. Katehakis and Arthur F. Veinott. 1987 · 1987
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Jensen-Shannon Divergence and Hilbert Space Embedding
Bent Fuglede and Flemming Topsoe. 2004 · 2004
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Matrix Factorization Techniques for Recommender Systems
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009 · 2009
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Item Popularity and Recommendation Accuracy. In Proceedings of the Fifth ACM Conference on Recommender Systems (RecSys ’11) . ACM, New York, NY, USA, 125–132
Harald Steck. 2011 · 2011
Cited alongside, same era.
Scalable Recommendation with Poisson Factorization
Prem Gopalan, Jake M. Hofman, and David M. Blei. 2013 · 2013
Cited alongside, same era.
Bayesian nonparametric poisson factorization for recommendation systems
Prem Gopalan, Francisco J.R. Ruiz, Rajesh Ranganath, and David M. Blei. 2014b · 2014
Cited alongside, same era.
Novelty and Diversity in Recommender Systems
Pablo Castells, Neil J. Hurley, and Saul Vargas. 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.
Recommendations as Treatments: Debiasing Learning and Evaluation
Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims. 2016 · 2016
Later among the works it cites.
Controlling Popularity Bias in Learning-to-Rank Recommendation. In Proceedings of the Eleventh ACM Conference on Recommender Systems (RecSys ’17) . ACM, New York, NY, USA, 42–46
Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2017 · 2017
Later among the works it cites.
How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18) . ACM, New York, NY, USA, 224–232
Allison J. B. Chaney, Brandon M. Stewart, and Barbara E. Engelhardt. 2018 · 2018
Later among the works it cites.
Iterated Algorithmic Bias in the Interactive Machine Learning Process of Information Filtering
Wenlong Sun, Olfa Nasraoui, and Patrick Shafto. 2018 · 2018
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Modeling User Exposure in Recommendation. In Proceedings of the 25th International Conference on World Wide Web (WWW ’16) . International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 951–961
Dawen Liang, Laurent Charlin, James McInerney, and David M. Blei. 2016 · 2016
Cited alongside, same era.
Content-based Recommendations with Poisson Factorization. In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2 (NIPS’14) . MIT Press, Cambridge, MA, USA, 3176–3184
Prem Gopalan, Laurent Charlin, and David M. Blei. 2014a
Cited in the paper.
Debiasing the Human-Recommender System Feedback Loop in Collaborative Filtering. Humbl2019
Olfa Nasraoui Wenlong Sun, Sami Khenissi and Patrick Shafto. [n. d.]
Cited in the paper.
SIREN: A Simulation Framework for Understanding the Effects of Recommender Systems in Online News Environments. In Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT* ’19) . ACM, New York, NY, USA, 150–159
Dimitrios Bountouridis, Jaron Harambam, Mykola Makhortykh, Mónica Marrero, Nava Tintarev, and Claudia Hauff. 2019 · 2019
Later among the works it cites.