{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T00:52:48Z","timestamp":1761094368233,"version":"build-2065373602"},"reference-count":12,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2017,10,24]],"date-time":"2017-10-24T00:00:00Z","timestamp":1508803200000},"content-version":"vor","delay-in-days":296,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["asistdl.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Proc. Assoc. Info. Sci. Tech."],"published-print":{"date-parts":[[2017,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:p>In large\u2010scale datasets, the researchers' multiple features need to be learned automatically instead of manually defined and enumerated, to improve the efficiency and effect of research collaboration prediction and recommendation. This paper applies the network embedding method to learn the context of each researcher by which the semantic similarities among researchers are calculated. Firstly, the co\u2010authorship network is built in a large\u2010scale dataset where research collaborations are denoted by co\u2010authorships. Then the researchers' semantic contexts in the network are learned by the network embedding method based on deep learning, and each researcher's dense, low\u2010dimensional vector is formed. Finally, the semantic similarities among researchers are calculated through vector similarity indices and quantitatively compared by link prediction. Experiments in the field of library and information science (LIS) verify that the method can improve the accuracy and effectiveness of research collaboration prediction and recommendation.<\/jats:p>","DOI":"10.1002\/pra2.2017.14505401182","type":"journal-article","created":{"date-parts":[[2017,10,24]],"date-time":"2017-10-24T03:35:36Z","timestamp":1508816136000},"page":"847-849","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Research collaboration prediction and recommendation based on network embedding in co\u2010authorship networks"],"prefix":"10.1002","volume":"54","author":[{"given":"Jinzhu","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Information Management Nanjing University of Science and Technology  China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2017,10,24]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/aje\/kwr441"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASONAM.2011.107"},{"key":"e_1_2_6_4_1","unstructured":"Goldberg Y. &Levy O.(2014).Word2vec explained: Deriving mikolov et al.'s negative\u2010sampling word\u2010embedding method. arXiv preprint arXiv:1402.3722."},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11192-013-1228-9"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.respol.2008.01.009"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2010.11.027"},{"key":"e_1_2_6_8_1","doi-asserted-by":"crossref","unstructured":"Perozzi B. Al\u2010Rfou R.&Skiena S.(2014).Deepwalk: Online learning of social representations. InProceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining(pp.701\u2013710):ACM.","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_2_6_9_1","doi-asserted-by":"crossref","unstructured":"Tang J. Qu M. Wang M. Zhang M. Yan J.&Mei Q.(2015).Line: Large\u2010scale information network embedding. 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