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| Research Interest Space Mining and Double-Task Research Recommendation Based on Heterogeneous Network Embedding |
| Cui Hongfei1, Feng Zihan2, Zhang Jingyu3 |
1.School of Economics and Management, University of Science and Technology Beijing, Beijing 100083
2.School of Economics and Management, Beihang University, Beijing 100191
3.University of Edinburgh Business School, the University of Edinburgh, Edinburgh EH8 9JU |
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Abstract The Internet contains rich literature databases that are crucial resources for researchers to understand various advances in their fields. Efficient information mining on a massive scale of research outcomes within specific fields can provide researchers globally with clearer directions amidst knowledge flow. In this regard, based on more than 130,000 scientific papers spanning 11 years (2010-2021) collected from a well-known biomedical literature database, PubMed, this study investigated the historical behavioral information of researchers. A heterogeneous information network containing authors, papers, and keywords was built, in which the nodes were subsequently embedded into a “heterogeneous research interest space” using the metapath2vec heterogeneous network embedding algorithm. Simultaneously, both research collaborator and keyword recommendation tasks were implemented based on the similarity metric between vectors in the space. Compared with existing studies, the proposed method can achieve double-task recommendation, obtaining not only meaningful results in the traditional research collaborator recommendation task but also significantly surpassing existing performance in heterogeneous keyword recommendation between researchers and keywords. Furthermore, an in-depth mining of author and keyword vectors in the space was conducted, proving that the heterogeneous research interest space can indeed provide inductive direction in understanding the semantic meaning of researchers’ interests in various research fields. The characteristics of the research interest space will provide new perspectives for researchers in terms of mining and comprehensive understanding of research interests.
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Received: 02 January 2023
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