{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T03:24:46Z","timestamp":1769743486574,"version":"3.49.0"},"reference-count":35,"publisher":"Association for Computing Machinery (ACM)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2015,1]]},"abstract":"<jats:p>\n            The recent surge in popularity of crowdsourcing has brought with it a new opportunity for engaging human intelligence in the process of data analysis. Crowdsourcing provides a fundamental mechanism for enabling online workers to participate in tasks that are either too difficult to be solved solely by a computer or too expensive to employ experts to perform. In the field of social science, four elements are required to form a wise crowd - Diversity of Opinion, Independence, Decentralization and Aggregation. However, while the other three elements are already studied and implemented in current crowdsourcing platforms, the 'Diversity of Opinion' has not been functionally enabled. In this paper, we address the algorithmic optimizations towards the\n            <jats:italic>diversity of opinion<\/jats:italic>\n            of crowdsourcing marketplaces.\n          <\/jats:p>\n          <jats:p>From a computational perspective, in order to build a wise crowd, we need to quantitatively modeling the diversity, and take it into consideration for constructing the crowd. In a crowdsourcing marketplace, we usually encounter two basic paradigms for worker selection: building a crowd to wait for tasks to come and selecting workers for a given task. Therefore, we propose our Similarity-driven Model (S-Model) and Task-driven Model (T-Model) for both of the paradigms. Under both of the models, we propose efficient and effective algorithms to enlist a budgeted number of workers, which have the optimal diversity. We have verified our solutions with extensive experiments on both synthetic datasets and real data sets.<\/jats:p>","DOI":"10.14778\/2735479.2735482","type":"journal-article","created":{"date-parts":[[2015,5,12]],"date-time":"2015-05-12T15:37:52Z","timestamp":1431445072000},"page":"485-496","source":"Crossref","is-referenced-by-count":37,"title":["Hear the whole story"],"prefix":"10.14778","volume":"8","author":[{"given":"Ting","family":"Wu","sequence":"first","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong, China"}]},{"given":"Lei","family":"Chen","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong, China"}]},{"given":"Pan","family":"Hui","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong, China"}]},{"given":"Chen Jason","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong, China"}]},{"given":"Weikai","family":"Li","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong, China"}]}],"member":"320","published-online":{"date-parts":[[2015,1]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"https:\/\/foursquare.com\/.  https:\/\/foursquare.com\/."},{"key":"e_1_2_1_2_1","unstructured":"https:\/\/petitions.whitehouse.gov\/.  https:\/\/petitions.whitehouse.gov\/."},{"key":"e_1_2_1_3_1","unstructured":"https:\/\/www.mturk.com\/mturk\/welcome.  https:\/\/www.mturk.com\/mturk\/welcome."},{"key":"e_1_2_1_4_1","unstructured":"http:\/\/www.crowdflower.com\/.  http:\/\/www.crowdflower.com\/."},{"key":"e_1_2_1_5_1","unstructured":"http:\/\/www.nltk.org\/.  http:\/\/www.nltk.org\/."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2463676.2465318"},{"key":"e_1_2_1_7_1","volume-title":"School of Computer Science","author":"Andreas Krause C. G.","year":"2005","unstructured":"C. G. Andreas Krause . A note on the budgeted maximization of submodular functions. Technical report , School of Computer Science , Carnegie Mellon University , March 2005 . C. G. Andreas Krause. A note on the budgeted maximization of submodular functions. 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R. Gomes, P. Welinder, A. Krause, and P. Perona. Crowdclustering. In NIPS, pages 558--566, 2011."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2213836.2213880"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.14778\/2536360.2536369"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-24777-7"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557074"},{"key":"e_1_2_1_21_1","volume-title":"MIT, Sloan School of Management","author":"Malone T.","year":"2009","unstructured":"T. Malone , R. Laubacher , and C. Dellarocas . Harnessing crowds: Mapping the genome of collective intelligence. Research Paper No. 4732-09 , MIT, Sloan School of Management , Massachusetts Institute of Technology , Cambridge, MA, USA , February 2009 . Sloan Research Paper No. 4732--09. T. Malone, R. Laubacher, and C. Dellarocas. Harnessing crowds: Mapping the genome of collective intelligence. Research Paper No. 4732-09, MIT, Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA, February 2009. Sloan Research Paper No. 4732--09."},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.14778\/2047485.2047487"},{"key":"e_1_2_1_23_1","volume-title":"Firms, Schools, and Societies","author":"Page S.","year":"2007","unstructured":"S. Page . The Difference: How the Power of Diversity Creates Better Groups , Firms, Schools, and Societies . Princeton University Press , 2007 . S. Page. The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies. Princeton University Press, 2007."},{"key":"e_1_2_1_24_1","volume-title":"Applying a logic of diversity","author":"Page S.","year":"2007","unstructured":"S. Page . Making the difference : Applying a logic of diversity . 2007 . S. Page. 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