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Bipartite matching, where agents on one side of a market are matched to agents or items on the other, is a classical problem in computer science and economics, with widespread application in healthcare, education, advertising, and general resource allocation.
An analysis of approximations for maximizing submodular set functions—i
George L Nemhauser, Laurence A Wolsey, and Marshall L Fisher · 1978
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Data path allocation based on bipartite weighted matching
Chu-Yi Huang, Yen-Shen Chen, Youn-Long Lin, and Yu-Chin Hsu · 1991
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Shape matching and object recognition using shape contexts
Serge Belongie, Jitendra Malik, and Jan Puzicha · 2002
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Secondary-structure matching (ssm), a new tool for fast protein structure alignment in three dimensions
E Krissinel and K Henrick · 2004
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Improving web search results using affinity graph
Benyu Zhang, Hua Li, Yi Liu, Lei Ji, Wensi Xi, Weiguo Fan, Zheng Chen, and Wei-Ying Ma · 2005
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Constrained multi-aspect expertise matching for committee review assignment
Maryam Karimzadehgan and ChengXiang Zhai · 2009
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Scholarly paper recommendation via user’s recent research interests
Kazunari Sugiyama and Min-Yen Kan · 2010
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Expertise matching via constraint-based optimization
Wenbin Tang, Jie Tang, and Chenhao Tan · 2010
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A class of submodular functions for document summarization
Hui Lin and Jeff Bilmes · 2011
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Improving aggregate recommendation diversity using ranking-based techniques
Gediminas Adomavicius and YoungOk Kwon · 2012
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Determinantal point processes for machine learning
Alex Kulesza and Ben Taskar · 2012
Cited alongside, same era.
Fairness, efficiency, and flexibility in organ allocation for kidney transplantation
Dimitris Bertsimas, Vivek F Farias, and Nikolaos Trichakis · 2013
Cited alongside, same era.
The Toronto paper matching system: an automated paper-reviewer assignment system
Laurent Charlin and Richard S Zemel · 2013
Cited alongside, same era.
Promoting diversity in recommendation by entropy regularizer
Lijing Qin and Xiaoyan Zhu · 2013
Cited alongside, same era.
An analysis of users’ propensity toward diversity in recommendations
Tommaso Di Noia, Vito Claudio Ostuni, Jessica Rosati, Paolo Tomeo, and Eugenio Di Sciascio · 2014
Cited alongside, same era.
A robust model for paper reviewer assignment
Controlled school choice with soft bounds and overlapping types
Ryoji Kurata, Masahiro Goto, Atsushi Iwasaki, and Makoto Yokoo · 2015
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MovieLens collaborative filtering
P. Allen Bradley · 2016
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Conflict-aware weighted bipartite b-matching and its application to e-commerce
Cheng Chen, Lan Zheng, Venkatesh Srinivasan, Alex Thomo, Kui Wu, and Anthony Sukow · 2016
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Gurobi optimizer reference manual, 2016
Inc. Gurobi Optimization · 2016
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The movielens datasets: History and context
F Maxwell Harper and Joseph A Konstan · 2016
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Text matching as image recognition
Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Shengxian Wan, and Xueqi Cheng · 2016
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Xiang Liu, Torsten Suel, and Nasir Memon · 2014
Cited alongside, same era.
Optimal greedy diversity for recommendation
Azin Ashkan, Branislav Kveton, Shlomo Berkovsky, and Zheng Wen · 2015
Cited alongside, same era.
FutureMatch: Combining human value judgments and machine learning to match in dynamic environments
John P. Dickerson and Tuomas Sandholm · 2015
Cited alongside, same era.
Sat is an effective and complete method for solving stable matching problems with couples
Joanna Drummond, Andrew Perrault, and Fahiem Bacchus · 2015
Cited alongside, same era.
A coverage-based approach to recommendation diversity on similarity graph
Shameem A. Puthiya Parambath, Nicolas Usunier, and Yves Grandvalet · 2016
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A framework for recommending relevant and diverse items
Chaofeng Sha, Xiaowei Wu, and Junyu Niu · 2016
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The effects of algorithmic labor market recommendations: evidence from a field experiment, 2017
John Joseph Horton · 2017
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