{"type":"ScholarlyArticle","title":"Diverse Weighted Bipartite b-Matching","authors":[{"name":"Faez Ahmed","slug":"faez-ahmed","given":"Faez","family":"Ahmed","affiliation":"Dept. of Mechanical Engg . University of Maryland"},{"name":"John P. Dickerson","slug":"john-p-dickerson","given":"John P.","family":"Dickerson","affiliation":"Dept. of Computer Sci . University of Maryland"},{"name":"Mark Fuge","slug":"mark-fuge","given":"Mark","family":"Fuge","affiliation":"Dept. of Mechanical Engg . University of Maryland"}],"abstract":"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.  A practitioner's goal is typically to maximize a matching market's economic efficiency, possibly subject to some fairness requirements that promote equal access to resources.  A natural balancing act exists between fairness and efficiency in matching markets, and has been the subject of much research.In this paper, we study a complementary goal---balancing diversity and efficiency---in a generalization of bipartite matching where agents on one side of the market can be matched to sets of agents on the other.  Adapting a classical definition of the diversity of a set, we propose a quadratic programming-based approach to solving a submodular minimization problem that balances diversity and total weight of the solution.  We also provide a scalable greedy algorithm with theoretical performance bounds.  We then define the price of diversity, a measure of the efficiency loss due to enforcing diversity, and give a worst-case theoretical bound.  Finally, we demonstrate the efficacy of our methods on three real-world datasets, and show that the price of diversity is not bad in practice.  Our code is publicly accessible for further research.","doi":"10.24963/ijcai.2017/6","url":"https://ijcai.org/proceedings/2017/6","pdf":"https://ijcai.org/proceedings/2017/0006.pdf","markdown":"https://ijcai.org/proceedings/2017/6.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/6","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Agent-based and Multi-agent Systems","track":"Main track","number":6,"pages":{"first":35,"last":41},"keywords":["Combinatorial & Heuristic Search: Combinatorial search/optimisation","Agent-based and Multi-agent Systems: Economic paradigms, auctions and market-based systems"],"videos":[],"references":[{"position":1,"raw":"[Adomavicius and Kwon, 2012] Gediminas Adomavicius and YoungOk Kwon. 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