Exploring dynamic research interest and academic influence for scientific collaborator recommendation

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.