当前位置: X-MOL 学术Knowl. Based Syst. › 论文详情
A multi-level fusion based decision support system for academic collaborator recommendation
Knowledge-Based Systems ( IF 5.101 ) Pub Date : 2020-03-20 , DOI: 10.1016/j.knosys.2020.105784
Tribikram Pradhan; Sukomal Pal

In academia, researchers collaborate with their peers to improve the quality of research and thereby enhance academic profiles. However, information overload in big scholarly data poses a challenge in identifying potential researchers for fruitful collaboration. In this article, we introduce a multi-level fusion-based model for collaborator recommendation, DRACoR (Deep learning and Random walk based Academic Collaborator Recommender). DRACoR fuses deep learning and biased random walk model to provide the recommendation for potential collaborators that share similar research interests at the peer level. We run a topic model on abstracts and Doc2Vec on titles on year-wise publications to capture the dynamic research interests of researchers. Author-author cosine similarity is computed from the feature vectors extracted from abstracts and titles and is then used to weigh edges in the author-author graph (AAG). We also aggregate various meta-path features with profile-aware features to bias the random walk behavior. Finally, we employ a random walk with restart(RWR) to recommend top N collaborators where the edge weights are used to bias the random walker’s behavior. Extensive experiments on DBLP and hep-th datasets demonstrate the effectiveness of our proposed DRACoR model against various state-of-the-art methods in terms of precision, recall, F1-score, MRR, and nDCG.
更新日期:2020-03-20

 

全部期刊列表>>
智控未来
聚焦商业经济政治法律
跟Nature、Science文章学绘图
控制与机器人
招募海内外科研人才,上自然官网
隐藏1h前已浏览文章
课题组网站
新版X-MOL期刊搜索和高级搜索功能介绍
ACS材料视界
x-mol收录
湖南大学化学化工学院刘松
上海有机所
李旸
南方科技大学
西湖大学
X-MOL
支志明
中山大学化学工程与技术学院
试剂库存
天合科研
down
wechat
bug