Career Age-Aware Scientific Collaborator Recommendation in Scholarly Big Data
Seeking a collaborator is one of the important academic activities of scholars because the right collaborators will help improve the quality of scholars’ research and accelerate their research process. Therefore, it is becoming more and more important to recommend scientific collaborators...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8835021/ |
| _version_ | 1811274368891748352 |
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| author | Na Sun Yong Lu Yongcun Cao |
| author_facet | Na Sun Yong Lu Yongcun Cao |
| author_sort | Na Sun |
| collection | DOAJ |
| description | Seeking a collaborator is one of the important academic activities of scholars because the right collaborators will help improve the quality of scholars’ research and accelerate their research process. Therefore, it is becoming more and more important to recommend scientific collaborators based on big scholarly data. However, previous works mainly consider the research topic as the key academic factor, whereas many scholars’ demographic characteristics such as career age, gender, etc are overlooked. It has been studied that scientific collaboration patterns may vary with scholars’ career ages. It is not surprising that scholars at different career ages may have different collaboration strategies. To this end, we aim to design a scientific collaboration recommendation model that is sensitive to scholars’ career age. For this purpose, we design a career age-aware scientific collaboration model. The model is mainly consisted of three parts, including authorship extraction from the digital libraries, topic extraction based on publication titles/abstract, and career age-aware random walk for measuring scholar similarity. Experimental results on two real-world datasets demonstrate that our proposed model can achieve the best performance by comparison with six baseline methods in terms of precision and recall. |
| first_indexed | 2024-04-12T23:17:45Z |
| format | Article |
| id | doaj.art-edbffcff64fa4c6d8bc9954435d0c90a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T23:17:45Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-edbffcff64fa4c6d8bc9954435d0c90a2022-12-22T03:12:37ZengIEEEIEEE Access2169-35362019-01-01713603613604510.1109/ACCESS.2019.29410228835021Career Age-Aware Scientific Collaborator Recommendation in Scholarly Big DataNa Sun0https://orcid.org/0000-0003-1449-8420Yong Lu1https://orcid.org/0000-0002-0241-8676Yongcun Cao2School of Information Engineering, Minzu University of China, Beijing, ChinaSchool of Information Engineering, Minzu University of China, Beijing, ChinaSchool of Information Engineering, Minzu University of China, Beijing, ChinaSeeking a collaborator is one of the important academic activities of scholars because the right collaborators will help improve the quality of scholars’ research and accelerate their research process. Therefore, it is becoming more and more important to recommend scientific collaborators based on big scholarly data. However, previous works mainly consider the research topic as the key academic factor, whereas many scholars’ demographic characteristics such as career age, gender, etc are overlooked. It has been studied that scientific collaboration patterns may vary with scholars’ career ages. It is not surprising that scholars at different career ages may have different collaboration strategies. To this end, we aim to design a scientific collaboration recommendation model that is sensitive to scholars’ career age. For this purpose, we design a career age-aware scientific collaboration model. The model is mainly consisted of three parts, including authorship extraction from the digital libraries, topic extraction based on publication titles/abstract, and career age-aware random walk for measuring scholar similarity. Experimental results on two real-world datasets demonstrate that our proposed model can achieve the best performance by comparison with six baseline methods in terms of precision and recall.https://ieeexplore.ieee.org/document/8835021/Scientific collaborationcareer agecollaborator recommendation |
| spellingShingle | Na Sun Yong Lu Yongcun Cao Career Age-Aware Scientific Collaborator Recommendation in Scholarly Big Data IEEE Access Scientific collaboration career age collaborator recommendation |
| title | Career Age-Aware Scientific Collaborator Recommendation in Scholarly Big Data |
| title_full | Career Age-Aware Scientific Collaborator Recommendation in Scholarly Big Data |
| title_fullStr | Career Age-Aware Scientific Collaborator Recommendation in Scholarly Big Data |
| title_full_unstemmed | Career Age-Aware Scientific Collaborator Recommendation in Scholarly Big Data |
| title_short | Career Age-Aware Scientific Collaborator Recommendation in Scholarly Big Data |
| title_sort | career age aware scientific collaborator recommendation in scholarly big data |
| topic | Scientific collaboration career age collaborator recommendation |
| url | https://ieeexplore.ieee.org/document/8835021/ |
| work_keys_str_mv | AT nasun careerageawarescientificcollaboratorrecommendationinscholarlybigdata AT yonglu careerageawarescientificcollaboratorrecommendationinscholarlybigdata AT yongcuncao careerageawarescientificcollaboratorrecommendationinscholarlybigdata |