{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T22:11:58Z","timestamp":1759961518267,"version":"3.41.2"},"reference-count":38,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,2,25]],"date-time":"2021-02-25T00:00:00Z","timestamp":1614211200000},"content-version":"vor","delay-in-days":55,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61876001","61602003","61673020"],"award-info":[{"award-number":["61876001","61602003","61673020"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002729","name":"Anhui University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002729","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>It is meaningful for a researcher to find some proper collaborators in complex academic tasks. Academic collaborator recommendation models are always based on the network embedding of academic collaborator networks. Most of them focus on the network structure, text information, and the combination of them. The latent semantic relationships exist according to the text information of nodes in the academic collaborator network. However, these relationships are often ignored, which implies the similarity of the researchers. How to capture the latent semantic relationships among researchers in the academic collaborator network is a challenge. In this paper, we propose a content\u2010enhanced network embedding model for academic collaborator recommendation, namely, CNEacR. We build a content\u2010enhanced academic collaborator network based on the weighted text representation of each researcher. The content\u2010enhanced academic collaborator network contains intrinsic collaboration relationships and latent semantic relationships. Firstly, the weighted text representation of each researcher is obtained according to its text information. Secondly, a content\u2010enhanced academic collaborator network is built via the similarity of the weighted text representation of researchers and intrinsic collaboration relationships. Thirdly, each researcher is represented as a latent vector using network representation learning. Finally, top\u2010<jats:italic>k<\/jats:italic> similar researchers are recommended for each target researcher. Experiment results on the real\u2010world datasets show that CNEacR achieves better performance than academic collaborator recommendation baselines.<\/jats:p>","DOI":"10.1155\/2021\/7035467","type":"journal-article","created":{"date-parts":[[2021,2,25]],"date-time":"2021-02-25T20:20:05Z","timestamp":1614284405000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Content\u2010Enhanced Network Embedding for Academic Collaborator Recommendation"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6474-9238","authenticated-orcid":false,"given":"Jie","family":"Chen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0361-7562","authenticated-orcid":false,"given":"Xin","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7647-3603","authenticated-orcid":false,"given":"Shu","family":"Zhao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5678-3038","authenticated-orcid":false,"given":"Yanping","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,2,25]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/tbdata.2016.2641460"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2017.03.006"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1177\/0306312705052359"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1177\/0306312705055535"},{"key":"e_1_2_10_5_2","doi-asserted-by":"crossref","unstructured":"MaH. 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