Exploring dynamic research interest and academic influence for scientific collaborator recommendation

Xiangjie Kong, Huizhen Jiang, Wei Wang, Teshome Megersa Bekele, Zhenzhen Xu () and Meng Wang
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Xiangjie Kong: Dalian University of Technology
Huizhen Jiang: Dalian University of Technology
Wei Wang: Dalian University of Technology
Teshome Megersa Bekele: Dalian University of Technology
Zhenzhen Xu: Dalian University of Technology
Meng Wang: Dalian University of Technology

Scientometrics, 2017, vol. 113, issue 1, No 18, 369-385

Abstract: Abstract In many cases, it is time-consuming for researchers to find proper collaborators who can provide researching guidance besides simply collaborating. The Most Beneficial Collaborators (MBCs), who have high academic level and relevant research topics, can genuinely help researchers to enrich their research. However, how can we find the MBCs? In this paper, we propose a most Beneficial Collaborator Recommendation model called BCR. BCR learns on researchers’ publications and associates three academic features: topic distribution of research interest, interest variation with time and researchers’ impact in collaborators network. We run a topic model on researchers’ publications in each year for topic clustering. The generated topic distribution matrix is fixed by a time function to fit the interest dynamic transformation. The academic social impact is also considered to mine the most prolific researchers. Finally, a TopN MBCs recommendation list is generated according to the computed score. Extensive experiments on a dataset with citation network demonstrate that, in comparison to relevant baseline approaches, our BCR performs better in terms of precision, recall, F1 score and the recommendation quality.

Keywords: Collaborator recommendation; Topic clustering; Research interest variation; Academic influence; Feature matrix (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (12)

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DOI: 10.1007/s11192-017-2485-9

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