{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T18:41:24Z","timestamp":1744828884478},"reference-count":0,"publisher":"National Library of Serbia","issue":"1","license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2017]]},"abstract":"<jats:p>A generalized ensemble model (gEnM) for document ranking is proposed in this\n   paper. The gEnM linearly combines the document retrieval models and tries to\n   retrieve relevant documents at high positions. In order to obtain the\n   optimal linear combination of multiple document retrieval models or rankers,\n   an optimization program is formulated by directly maximizing the mean\n   average precision. Both supervised and unsupervised learning algorithms are\n   presented to solve this program. For the supervised scheme, two approaches\n   are considered based on the data setting, namely batch and online setting.\n   In the batch setting, we propose a revised Newton?s algorithm, gEnM.BAT, by\n   approximating the derivative and Hessian matrix. In the online setting, we\n   advocate a stochastic gradient descent (SGD) based algorithm-gEnM.ON. As for\n   the unsupervised scheme, an unsupervised ensemble model (UnsEnM) by\n   iteratively co-learning from each constituent ranker is presented.\n   Experimental study on benchmark data sets verifies the effectiveness of the\n   proposed algorithms. Therefore, with appropriate algorithms, the gEnM is a\n   viable option in diverse practical information retrieval applications.<\/jats:p>","DOI":"10.2298\/csis160229042w","type":"journal-article","created":{"date-parts":[[2016,12,30]],"date-time":"2016-12-30T07:21:13Z","timestamp":1483082473000},"page":"123-151","source":"Crossref","is-referenced-by-count":2,"title":["Generalized ensemble model for document ranking in information retrieval"],"prefix":"10.2298","volume":"14","author":[{"given":"Yanshan","family":"Wang","sequence":"first","affiliation":[{"name":"Mayo Clinic, Department of Health Sciences Research, Rochester, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"In-Chan","family":"Choi","sequence":"additional","affiliation":[{"name":"Korea University, School of Industrial Management Engineering, SeouL, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongfang","family":"Liu","sequence":"additional","affiliation":[{"name":"Mayo Clinic, Department of Health Sciences Research, Rochester, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1078","container-title":["Computer Science and Information Systems"],"original-title":[],"language":"en","deposited":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T08:33:00Z","timestamp":1685349180000},"score":1,"resource":{"primary":{"URL":"https:\/\/doiserbia.nb.rs\/Article.aspx?ID=1820-02141600042W"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"references-count":0,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2017]]}},"URL":"https:\/\/doi.org\/10.2298\/csis160229042w","relation":{},"ISSN":["1820-0214","2406-1018"],"issn-type":[{"value":"1820-0214","type":"print"},{"value":"2406-1018","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017]]}}}