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Simulation has emerged as a popular method to study the long-term societal consequences of recommender systems.
RecSim: A Configurable Simulation Platform for Recommender Systems
Eugene Ie, Chih wei Hsu, Martin Mladenov, Vihan Jain, Sanmit Narvekar, Jing Wang, Rui Wu, and Craig Boutilier. 2019 · 1909
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Dynamic models of segregation
Thomas C Schelling. 1971 · 1971
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Animating rotation with quaternion curves. In Proceedings of the 12th annual conference on Computer graphics and interactive techniques . 245–254
Ken Shoemake. 1985 · 1985
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How to compute the Wiener index of a graph
Bojan Mohar and Tomaž Pisanski. 1988 · 1988
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Growing artificial societies: social science from the bottom up
Joshua M Epstein and Robert Axtell. 1996 · 1996
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The complexity of cooperation: Agent-based models of competition and collaboration . Vol. 3
Robert Axelrod. 1997 · 1997
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Learning from the behavior of others: Conformity, fads, and informational cascades
Sushil Bikhchandani, David Hirshleifer, and Ivo Welch. 1998 · 1998
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Modelling and simulation software to support individual-based ecological modelling
Helmut Lorek and Michael Sonnenschein. 1999 · 1999
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Republic.com
Cass R. Sunstein. 2001 · 2001
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Agent-based modeling: Methods and techniques for simulating human systems
Eric Bonabeau. 2002 · 2002
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Getting to know you: learning new user preferences in recommender systems. In Proceedings of the 7th international conference on Intelligent User Interfaces . 127–134
Al Mamunur Rashid, Istvan Albert, Dan Cosley, Shyong K Lam, Sean M McNee, Joseph A Konstan, and John Riedl. 2002 · 2002
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Evaluating collaborative filtering recommender systems
Jonathan L Herlocker, Joseph A Konstan, Loren G Terveen, and John T Riedl. 2004 · 2004
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Netlogo: A simple environment for modeling complexity. In International conference on complex systems , Vol. 21. Boston, MA, 16–21
Seth Tisue and Uri Wilensky. 2004 · 2004
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Cai-Nicolas Ziegler, Sean M McNee, Joseph A Konstan, and Georg Lausen. 2005 · 2005
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Experimental study of inequality and unpredictability in an artificial cultural market
Matthew J Salganik, Peter Sheridan Dodds, and Duncan J Watts. 2006 · 2006
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Collaborative filtering for implicit feedback datasets. In 2008 Eighth IEEE International Conference on Data Mining . Ieee, 263–272
Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008 · 2008
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Novelty and diversity in top-n recommendation–analysis and evaluation
Neil Hurley and Mi Zhang. 2011 · 2011
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The filter bubble: How the new personalized web is changing what we read and how we think
Eli Pariser. 2011 · 2011
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Scikit-learn: Machine Learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011 · 2011
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Introduction to recommender systems handbook
Francesco Ricci, Lior Rokach, and Bracha Shapira. 2011 · 2011
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BPR: Bayesian personalized ranking from implicit feedback
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012 · 2012
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Measuring personalization of web search. In Proceedings of the 22nd international conference on World Wide Web . 527–538
Aniko Hannak, Piotr Sapiezynski, Arash Molavi Kakhki, Balachander Krishnamurthy, David Lazer, Alan Mislove, and Christo Wilson. 2013 · 2013
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Multi-method modelling: AnyLogic
Andrei Borshchev, Sally Brailsford, Leonid Churilov, and Brian Dangerfield. 2014 · 2014
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Agent-based models
Scott De Marchi and Scott E Page. 2014 · 2014
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Rumor cascades. In Proceedings of the International AAAI Conference on Web and Social Media , Vol. 8
Adrien Friggeri, Lada Adamic, Dean Eckles, and Justin Cheng. 2014 · 2014
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Opinion formation in the digital divide
Dongwon Lim, Hwansoo Lee, Hang-Jung ZO, and Andrew Ciganek. 2014 · 2014
Earlier work this paper cites.
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
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Exposure to ideologically diverse news and opinion on Facebook
Eytan Bakshy, Solomon Messing, and Lada A Adamic. 2015 · 2015
Earlier work this paper cites.
The movielens datasets: History and context
F Maxwell Harper and Joseph A Konstan. 2015 · 2015
Cited alongside, same era.
Agent-based models and microsimulation
Daniel Heard, Gelonia Dent, Tracy Schifeling, and David Banks. 2015 · 2015
Cited alongside, same era.
Collaborative deep learning for recommender systems. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining . 1235–1244
Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015 · 2015
Cited alongside, same era.
Ensemble-based and hybrid recommender systems
Charu C Aggarwal. 2016 · 2016
Cited alongside, same era.
Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems . 7–10
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et al · 2016
Cited alongside, same era.
Searching for alternative facts
Francesca Tripodi. 2018 · 2018
Later among the works it cites.
YouTube, the Great Radicalizer
Zeynep Tufekci. 2018 · 2018
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The spread of true and false news online
Soroush Vosoughi, Deb Roy, and Sinan Aral. 2018 · 2018
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DRN: A deep reinforcement learning framework for news recommendation. In Proceedings of the 2018 World Wide Web Conference . 167–176
Guanjie Zheng, Fuzheng Zhang, Zihan Zheng, Yang Xiang, Nicholas Jing Yuan, Xing Xie, and Zhenhui Li. 2018 · 2018
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Discrimination through optimization: How Facebook’s Ad delivery can lead to biased outcomes
Muhammad Ali, Piotr Sapiezynski, Miranda Bogen, Aleksandra Korolova, Alan Mislove, and Aaron Rieke. 2019 · 2019
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On the scientific superiority of conceptual replications for scientific progress
Christian S. Crandall and Jeffrey W. Sherman. 2016 · 2016
Cited alongside, same era.
The structural virality of online diffusion
Sharad Goel, Ashton Anderson, Jake Hofman, and Duncan J. Watts. 2016 · 2016
Cited alongside, same era.
The Netflix Recommender System: Algorithms, Business Value, and Innovation
Carlos A. Gomez-Uribe and Neil Hunt. 2016 · 2016
Cited alongside, same era.
The Problem Isn’t Fake News, It’s Bad Algorithms — Here’s Why
Ryan Holmes. 2016 · 2016
Cited alongside, same era.
Recommendations with a purpose. In Proceedings of the 10th ACM conference on recommender systems . 7–10
Dietmar Jannach and Gediminas Adomavicius. 2016 · 2016
Cited alongside, same era.
An agent-based model of message propagation in the Facebook electronic social network. preprint
HR Nasrinpour, MR Friesen, and RD McLeod. 2016 · 2016
Cited alongside, same era.
Multisided fairness for recommendation
Robin Burke. 2017 · 2017
Cited alongside, same era.
Dimitrios Bountouridis, Jaron Harambam, Mykola Makhortykh, Mónica Marrero, Nava Tintarev, and Claudia Hauff. 2019 · 2019
Later among the works it cites.
Playing the visibility game: How digital influencers and algorithms negotiate influence on Instagram
Kelley Cotter. 2019 · 2019
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How YouTube Radicalized Brazil
Max Fischer and Amanda Taub. 2019 · 2019
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The triple-filter bubble: Using agent-based modelling to test a meta-theoretical framework for the emergence of filter bubbles and echo chambers
Daniel Geschke, Jan Lorenz, and Peter Holtz. 2019 · 2019
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Powered by AI: Instagram’s Explore recommender system
Taylor Gordon Ivan Medvedev, Haotian Wu. 2019 · 2019
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Degenerate feedback loops in recommender systems. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society . 383–390
Ray Jiang, Silvia Chiappa, Tor Lattimore, András György, and Pushmeet Kohli. 2019 · 2019
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Homophily and minority-group size explain perception biases in social networks
Eun Lee, Fariba Karimi, Claudia Wagner, Hang-Hyun Jo, Markus Strohmaier, and Mirta Galesic. 2019 · 2019
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Alt-right pipeline: Individual journeys to extremism online
Luke Munn. 2019 · 2019
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Modelling opinion dynamics in the age of algorithmic personalisation
Nicola Perra and Luis EC Rocha. 2019 · 2019
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Debiasing the human-recommender system feedback loop in collaborative filtering. In Companion Proceedings of The 2019 World Wide Web Conference . 645–651
Wenlong Sun, Sami Khenissi, Olfa Nasraoui, and Patrick Shafto. 2019 · 2019
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Multistakeholder recommendation: Survey and research directions
Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, and Luiz Pizzato. 2020 · 2020
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Deconstructing the Filter Bubble: User Decision-Making and Recommender Systems. In Fourteenth ACM Conference on Recommender Systems . 82–91
Guy Aridor, Duarte Goncalves, and Shan Sikdar. 2020 · 2020
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Fairness is not static: deeper understanding of long term fairness via simulation studies. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency
Alexander D’Amour, Hansa Srinivasan, James Atwood, Pallavi Baljekar, D Sculley, and Yoni Halpern. 2020 · 2020
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Recommendations and user agency: the reachability of collaboratively-filtered information. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency . 436–445
Sarah Dean, Sarah Rich, and Benjamin Recht. 2020 · 2020
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LensKit for Python: Next-Generation Software for Recommender Systems Experiments. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management . 2999–3006
Michael D Ekstrand. 2020 · 2020
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Array programming with NumPy
Charles R. Harris, K. Jarrod Millman, Stéfan J van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus, Stephan Hoyer, Marten H. van Kerkwijk, Matthew Brett, Allan Haldane, Jaime Fernández del Río, Mark Wiebe, Pearu Peterson, Pierre Gérard-Marchant, Kevin Sheppard, Tyler Reddy, Warren Weckesser, Hameer Abbasi, Christoph Gohlke, and Travis E. Oliphant. 2020 · 2020
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Evaluating the scale, growth, and origins of right-wing echo chambers on YouTube
Homa Hosseinmardi, Amir Ghasemian, Aaron Clauset, David M Rothschild, Markus Mobius, and Duncan J Watts. 2020 · 2020
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Do Offline Metrics Predict Online Performance in Recommender Systems?
Karl Krauth, Sarah Dean, Alex Zhao, Wenshuo Guo, Mihaela Curmei, Benjamin Recht, and Michael I Jordan. 2020 · 2020
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Right-Wing YouTube: A Supply and Demand Perspective
Kevin Munger and Joseph Phillips. 2020 · 2020
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Auditing radicalization pathways on YouTube. In Proceedings of the 2020 conference on fairness, accountability, and transparency . 131–141
Manoel Horta Ribeiro, Raphael Ottoni, Robert West, Virgílio AF Almeida, and Wagner Meira Jr. 2020 · 2020
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SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C J Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors. 2020 · 2020
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RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems
Martin Mladenov, Chih-Wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, and Craig Boutilier. 2021 · 2021
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