An Approach to Mining Scholars' Research Interests based on Academic Papers and Citation Networks

In the study of scholars, the research interests of scholars have strong personalized features, which can well reflect the interest preferences of scholars in specific research fields to serve the tasks of user profiling and academic recommendation of scholars. Research interest mining aims to mine topics that can be used to describe scholars' research interests directly from the text. The existing methods lack the comprehensive use of semantic information of academic papers and academic network information. In this paper, we construct a research interest mining method for scholars based on the text of their papers and citation networks. On the one hand, the LSI model and Doc2Vec model are used for text representation of scholars' papers, which can effectively extract the topics of papers and mine the semantic information in the text; on the other hand, the MinHash algorithm is used to reduce the complexity of citation network similarity calculation. Finally, the integration method is used for fusion of interest labels to obtain the final scholars' research interest labels. The experimental results demonstrate that the integrated approach can get higher accuracy rates.