Journal
IEEE ACCESS
Volume 6, Issue -, Pages 20298-20308Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2018.2823720
Keywords
Recommender system; correlation; co-author relationship; collaboration; co-author; trust
Categories
Funding
- National Natural Science Foundation of China [61632009, 61472451]
- Guangdong Provincial Natural Science Foundation [2017A030308006]
- High-Level Talents Program of Higher Education in Guangdong Province [2016ZJ01]
Ask authors/readers for more resources
Recommender systems have roots in numerous fields, and their use is widespread in the modern world. The scientific community is striving to enhance the quality of life by breaking innovative barriers and developing solutions that had never previously been considered. In an ideal world, an individual researcher would participate in various fields of research and make cumulative impactful contributions to bene fit society. However, in reality, this goal is difficult to attain without a team of collaborators. Collaboration refers to the information of partnerships that bring uniquely talented researchers together around a common idea. However, efforts to seek such co-authors not only are challenging but also occasionally yield no significant results. In this paper, we propose a recommender system to aggregate author information from multiple publisher networks. It evaluates the trustworthiness of the author recommendations based on the impact of the authors' contributions and the recency and popularity of their work as well as the correlations among these factors. On this basis, the system generates a list of prospective collaborators who might be of interest to a given researcher.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available