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Recommender systems can strongly influence which information we see online, e.g., on social media, and thus impact our beliefs, decisions, and actions.
Bias in computer systems
Batya Friedman and Helen Nissenbaum · 1996
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Justice as fairness: A restatement
John Rawls · 2001
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Measuring racial discrimination
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E-commerce product recommendation agents: Use, characteristics, and impact
Bo Xiao and Izak Benbasat · 2007
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Discrimination-aware data mining
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Systematic literature reviews in software engineering - A systematic literature review
Barbara A. Kitchenham, Pearl Brereton, David Budgen, Mark Turner, John Bailey, and Stephen G. Linkman · 2009
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Recommender Systems - An Introduction
Dietmar Jannach, Markus Zanker, Alexander Felfernig, and Gerhard Friedrich · 2010
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Novel recommendation based on personal popularity tendency
Jinoh Oh, Sun Park, Hwanjo Yu, Min Song, and Seung-Taek Park · 2011
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard S. Zemel · 2012
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Recommender systems in computer science and information systems - a landscape of research
Dietmar Jannach, Markus Zanker, Mouzhi Ge, and Marian Gröning · 2012
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Context-aware recommender systems
Gediminas Adomavicius and Alexander Tuzhilin · 2015
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Privacy aspects of recommender systems
Arik Friedman, Bart P Knijnenburg, Kris Vanhecke, Luc Martens, and Shlomo Berkovsky · 2015
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The MovieLens Datasets: History and Context
F. Maxwell Harper and Joseph A. Konstan · 2015
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What recommenders recommend: an analysis of recommendation biases and possible countermeasures
Dietmar Jannach, Lukas Lerche, Iman Kamehkhosh, and Michael Jugovac · 2015
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People-to-people reciprocal recommenders
Irena Koprinska and Kalina Yacef · 2015
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Machine bias
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
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The case for process fairness in learning: Feature selection for fair decision making
Nina Grgic-Hlaca, Muhammad Bilal Zafar, Krishna P Gummadi, and Adrian Weller · 2016
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Recommender systems - beyond matrix completion
Dietmar Jannach, Paul Resnick, Alexander Tuzhilin, and Markus Zanker · 2016
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Balanced neighborhoods for fairness-aware collaborative recommendation
Robin Burke, Nasim Sonboli, Masoud Mansoury, and Aldo Ordoñez-Gauger · 2017
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Multisided fairness for recommendation
Robin Burke · 2017
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Who Makes Trends? Understanding Demographic Biases in Crowdsourced Recommendations
Abhijnan Chakraborty, Johnnatan Messias, Fabrício Benevenuto, Saptarshi Ghosh, Niloy Ganguly, and Krishna P. Gummadi · 2017
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova · 2017
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Price and profit awareness in recommender systems
Dietmar Jannach and Gediminas Adomavicius · 2017
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Efficient optimization of multiple recommendation quality factors according to individual user tendencies
Michael Jugovac, Dietmar Jannach, and Lukas Lerche · 2017
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Inherent trade-offs in the fair determination of risk scores
Jon M. Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2017
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Counterfactual fairness
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva · 2017
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A systematic review and taxonomy of explanations in decision support and recommender systems
Ingrid Nunes and Dietmar Jannach · 2017
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The price of fairness in location based advertising
Christopher Riederer and Augustin Chaintreau · 2017
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Fairness in package-to-group recommendations
Dimitris Serbos, Shuyao Qi, Nikos Mamoulis, Evaggelia Pitoura, and Panayiotis Tsaparas · 2017
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Fairness in group recommendations in the health domain
Maria Stratigi, Haridimos Kondylakis, and Kostas Stefanidis · 2017
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Beyond parity: Fairness objectives for collaborative filtering
Sirui Yao and Bert Huang · 2017
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Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi · 2017
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Bias on the web
Ricardo Baeza-Yates · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
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Balanced neighborhoods for multi-sided fairness in recommendation
Robin Burke, Nasim Sonboli, and Aldo Ordonez-Gauger · 2018
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The measure and mismeasure of fairness: A critical review of fair machine learning
Sam Corbett-Davies and Sharad Goel · 2018
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An observational user study for group recommender systems in the tourism domain
Amra Delic, Julia Neidhardt, Thuy Ngoc Nguyen, and Francesco Ricci · 2018
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A fairness-aware hybrid recommender system
Golnoosh Farnadi, Pigi Kouki, Spencer K. Thompson, Sriram Srinivasan, and Lise Getoor · 2018
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Group Recommender Systems: An Introduction
Alexander Felfernig, Ludovico Boratto, Martin Stettinger, and Marko Tkali · 2018
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State of the art: Reproducibility in artificial intelligence
Odd Erik Gundersen and Sigbjørn Kjensmo · 2018
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Efficient and fair item coverage in recommender systems
Iordanis Koutsopoulos and Maria Halkidi · 2018
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Towards a fair marketplace: Counterfactual evaluation of the trade-off between relevance, fairness & satisfaction in recommendation systems
Rishabh Mehrotra, James McInerney, Hugues Bouchard, Mounia Lalmas, and Fernando Diaz · 2018
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21 definitions of fairness and their politics
Arvind Narayanan · 2018
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David Rohde, Stephen Bonner, Travis Dunlop, Flavian Vasile, and Alexandros Karatzoglou · 2018
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Calibrated recommendations
Harald Steck · 2018
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Fairness definitions explained
Sahil Verma and Julia Rubin · 2018
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Fairness in decision-making—the causal explanation formula
Junzhe Zhang and Elias Bareinboim · 2018
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Fairness in reciprocal recommendations: A speed-dating study
Yong Zheng, Tanaya Dave, Neha Mishra, and Harshit Kumar · 2018
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FMSR: A fairness-aware mobile service recommendation method
Qiliang Zhu, Ao Zhou, Qibo Sun, Shangguang Wang, and Fangchun Yang · 2018
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Fairness-aware tensor-based recommendation
Ziwei Zhu, Xia Hu, and James Caverlee · 2018
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Fairness-aware recommendation of information curators
Ziwei Zhu, Jianling Wang, Yin Zhang, and James Caverlee · 2018
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Multi-stakeholder recommendation and its connection to multi-sided fairness
Himan Abdollahpouri and Robin Burke · 2019
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Managing popularity bias in recommender systems with personalized re-ranking
Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher · 2019
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The unfairness of popularity bias in recommendation
Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher · 2019
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Fairness and Machine Learning
Solon Barocas, Moritz Hardt, and Arvind Narayanan · 2019
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Fairness in recommendation ranking through pairwise comparisons
Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, and Cristos Goodrow · 2019
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Equality of voice: Towards fair representation in crowdsourced top-k recommendations
Abhijnan Chakraborty, Gourab K. Patro, Niloy Ganguly, Krishna P. Gummadi, and Patrick Loiseau · 2019
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Recommender systems fairness evaluation via generalized cross entropy
Yashar Deldjoo, Vito Walter Anelli, Hamed Zamani, Alejandro Bellogín Kouki, and Tommaso Di Noia · 2019
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Music cold-start and long-tail recommendation: bias in deep representations
Andres Ferraro · 2019
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Fairness-aware ranking in search & recommendation systems with application to linkedin talent search
Sahin Cem Geyik, Stuart Ambler, Krishnaram Kenthapadi, and George Karypis · 2019
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Measuring the business value of recommender systems
Dietmar Jannach and Michael Jugovac · 2019
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Crank up the volume: Preference bias amplification in collaborative recommendation
Kun Lin, Nasim Sonboli, Bamshad Mobasher, and Robin Burke · 2019
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Bias disparity in collaborative recommendation: Algorithmic evaluation and comparison
Masoud Mansoury, Bamshad Mobasher, Robin Burke, and Mykola Pechenizkiy · 2019
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Explanation in artificial intelligence: Insights from the social sciences
Tim Miller · 2019
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Auditing and achieving intersectional fairness in classification problems
Giulio Morina, Viktoriia Oliinyk, Julian Waton, Ines Marusic, and Konstantinos Georgatzis · 2019
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Characterizing intersectional group fairness with worst-case comparisons
Avijit Ghosh, Lea Genuit, and Mary Reagan · 2021
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Fairness in network-friendly recommendations
Theodoros Giannakas, Pavlos Sermpezis, Anastasios Giovanidis, Thrasyvoulos Spyropoulos, and George Arvanitakis · 2021
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The winner takes it all: Geographic imbalance and provider (un)fairness in educational recommender systems
Elizabeth Gómez, Carlos Shui Zhang, Ludovico Boratto, Maria Salamó, and Mirko Marras · 2021
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On the problem of underranking in group-fair ranking
Sruthi Gorantla, Amit Deshpande, and Anand Louis · 2021
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Online post-processing in rankings for fair utility maximization
Ananya Gupta, Eric Johnson, Justin Payan, Aditya Kumar Roy, Ari Kobren, Swetasudha Panda, Jean-Baptiste Tristan, and Michael Wick · 2021
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This thing called fairness: Disciplinary confusion realizing a value in technology
Deirdre K. Mulligan, Joshua A. Kroll, Nitin Kohli, and Richmond Y. Wong · 2019
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Social data: Biases, methodological pitfalls, and ethical boundaries
Alexandra Olteanu, Carlos Castillo, Fernando Diaz, and Emre Kiciman · 2019
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Fighting fire with fire: Using antidote data to improve polarization and fairness of recommender systems
Bashir Rastegarpanah, Krishna P Gummadi, and Mark Crovella · 2019
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Fairness and abstraction in sociotechnical systems
Andrew D. Selbst, Danah Boyd, Sorelle A. Friedler, Suresh Venkatasubramanian, and Janet Vertesi · 2019
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Fairness in algorithmic decision-making: Applications in multi-winner voting, machine learning, and recommender systems
Yash Raj Shrestha and Yongjie Yang · 2019
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Mathematical notions vs. human perception of fairness: A descriptive approach to fairness for machine learning
Megha Srivastava, Hoda Heidari, and Andreas Krause · 2019
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Debiasing the human-recommender system feedback loop in collaborative filtering
Wenlong Sun, Sami Khenissi, Olfa Nasraoui, and Patrick Shafto · 2019
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Qianxiu Hao, Qianqian Xu, Zhiyong Yang, and Qingming Huang · 2021
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Perception of fairness in group music recommender systems
Nyi Nyi Htun, Elisa Lecluse, and Katrien Verbert · 2021
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Recommender systems: Past, present, future
Dietmar Jannach, Pearl Pu, Francesco Ricci, and Markus Zanker · 2021
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Estimation of fair ranking metrics with incomplete judgments
Ömer Kirnap, Fernando Diaz, Asia Biega, Michael D. Ekstrand, Ben Carterette, and Emine Yilmaz · 2021
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User-oriented fairness in recommendation
Yunqi Li, Hanxiong Chen, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang · 2021
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Towards personalized fairness based on causal notion
Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, and Yongfeng Zhang · 2021
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Tutorial on fairness of machine learning in recommender systems
Yunqi Li, Yingqiang Ge, and Yongfeng Zhang · 2021
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Mitigating sentiment bias for recommender systems
Chen Lin, Xinyi Liu, Guipeng Xv, and Hui Li · 2021
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Fairness-preserving group recommendations with user weighting
Ladislav Malecek and Ladislav Peska · 2021
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A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2021
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Investigating gender fairness of recommendation algorithms in the music domain
Alessandro B. Melchiorre, Navid Rekabsaz, Emilia Parada-Cabaleiro, Stefan Brandl, Oleg Lesota, and Markus Schedl · 2021
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Bias-aware hierarchical clustering for detecting the discriminated groups of users in recommendation systems
Joanna Misztal-Radecka and Bipin Indurkhya · 2021
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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
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CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation
Ruihong Qiu, Sen Wang, Zhi Chen, Hongzhi Yin, and Zi Huang · 2021
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Diversity-aware Recommendations for Social Justice? Exploring User Diversity and Fairness in Recommender Systems
Laura Schelenz · 2021
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A unified optimization toolbox for solving popularity bias, fairness, and diversity in recommender systems
Sinan Seymen, Himan Abdollahpouri, and Edward C. Malthouse · 2021
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Towards user-oriented privacy for recommender system data: A personalization-based approach to gender obfuscation for user profiles
Manel Slokom, Alan Hanjalic, and Martha Larson · 2021
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Fairness and transparency in recommendation: The users’ perspective
Nasim Sonboli, Jessie J. Smith, Florencia Cabral Berenfus, Robin Burke, and Casey Fiesler · 2021
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Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring
Tom Sühr, Sophie Hilgard, and Himabindu Lakkaraju · 2021
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Practical compositional fairness: Understanding fairness in multi-component recommender systems
Xuezhi Wang, Nithum Thain, Anu Sinha, Flavien Prost, Ed H Chi, Jilin Chen, and Alex Beutel · 2021
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Fairness-aware news recommendation with decomposed adversarial learning
Chuhan Wu, Fangzhao Wu, Xiting Wang, Yongfeng Huang, and Xing Xie · 2021
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TFROM: A two-sided fairness-aware recommendation model for both customers and providers
Yao Wu, Jian Cao, Guandong Xu, and Yudong Tan · 2021
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Cluster-based quotas for fairness improvements in music recommendation systems
Bruna D. Wundervald · 2021
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Policy-gradient training of fair and unbiased ranking functions
Himank Yadav, Zhengxiao Du, and Thorsten Joachims · 2021
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Longitudinal impact of preference biases on recommender systems’ performance
Meizi Zhou, Jingjng Zhang, and Gediminas Adomavicius · 2021
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Fairness among new items in cold start recommender systems
Ziwei Zhu, Jingu Kim, Trung Nguyen, Aish Fenton, and James Caverlee · 2021
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