{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T15:29:44Z","timestamp":1772724584752,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,8]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2017\/6","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T05:14:07Z","timestamp":1501218847000},"page":"35-41","source":"Crossref","is-referenced-by-count":21,"title":["Diverse Weighted Bipartite b-Matching"],"prefix":"10.24963","author":[{"given":"Faez","family":"Ahmed","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, University of Maryland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John P.","family":"Dickerson","sequence":"additional","affiliation":[{"name":"Computer Science Department, University of Maryland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark","family":"Fuge","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Maryland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Twenty-Sixth International Joint Conference on Artificial Intelligence","theme":"Artificial Intelligence","location":"Melbourne, Australia","acronym":"IJCAI-2017","number":"26","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)","University of Technology Sydney (UTS)","Australian Computer Society (ACS)"],"start":{"date-parts":[[2017,8,19]]},"end":{"date-parts":[[2017,8,26]]}},"container-title":["Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T07:51:48Z","timestamp":1501228308000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2017\/6"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2017,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2017\/6","relation":{},"subject":[],"published":{"date-parts":[[2017,8]]}}}