CollabSeer

Hung-Hsuan Chen, Liang Gou, Xiaolong Zhang, Clyde Lee Giles
2011 Proceeding of the 11th annual international ACM/IEEE joint conference on Digital libraries - JCDL '11  
Collaborative research has been increasingly popular and important in academic circles. However, there is no open platform available for scholars or scientists to effectively discover potential collaborators. This paper discusses Collab-Seer, an open system to recommend potential research collaborators for scholars and scientists. CollabSeer discovers collaborators based on the structure of the coauthor network and a user's research interests. Currently, three different network structure
more » ... s methods that use vertex similarity are supported in CollabSeer: Jaccard similarity, cosine similarity, and our relation strength similarity measure. Users can also request a recommendation by selecting a topic of interest. The topic of interest list is determined by CollabSeer's lexical analysis module, which analyzes the key phrases of previous publications. The CollabSeer system is highly modularized making it easy to add or replace the network analysis module or users' topic of interest analysis module. CollabSeer integrates the results of the two modules to recommend collaborators to users. Initial experimental results over the a subset of the CiteSeerX database shows that CollabSeer can efficiently discover prospective collaborators.
doi:10.1145/1998076.1998121 dblp:conf/jcdl/ChenGZG11 fatcat:ksrhk5lmj5hxdeplgyzjp52h7e