Abstract: Collaborator recommendation is a crucial topic in research management. This paper proposes a Novelty-Oriented Scientific Research Collaborator recommendation model (NovSRC). By recommending collaborators under the guidance of novel indicators, NovSRC aims to broaden scholars' research perspectives and facilitate the progress of research innovation. NovSRC utilizes heterogeneous academic networks composed of different academic entities and their relationships to learn vector representations of scholars and quantify their novelty metrics. A weighted academic collaboration network was constructed by measuring the novelty collaboration strength (NCS) among scholars under the novelty index, and based on this network, the final vector representation of scholars under the guidance of novelty characteristics was learned. By calculating the similarity between scholar vectors, NovSRC generates a Top-N recommendation list with a focus on novelty. The experimental results indicate that NovSRC achieved the best recommendation performance. Compared with the baseline models, the recommendation precision of NovSRC has improved by 6.9%, the F1 value has increased by 17.3%, and the novelty collaboration strength among scholars has increased by 3.3%. The analysis of the recommended list shows that compared to the target scholars, scholars recommended by the NovSRC model exhibit a wider distribution of research interests, which confirms that novelty has become a key benchmark factor for scholars seeking collaborators.

Xiuxiu Li, Mingyang Wang, Chaoran Wang, Yujia Fu and Xianjie Wang, “NovSRC: A Novelty-Oriented Scientific Collaborators Recommendation Model” International Journal of Advanced Computer Science and Applications(IJACSA), 15(3), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150366

@article{Li2024,
title = {NovSRC: A Novelty-Oriented Scientific Collaborators Recommendation Model},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150366},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150366},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {3},
author = {Xiuxiu Li and Mingyang Wang and Chaoran Wang and Yujia Fu and Xianjie Wang}
}