Name
Abstract | ||
|---|---|---|
Most of the previous studies on scientific collaborator recommendation are based on social proximity analysis to suggest collaborators. However, the extracted homogeneous features cannot well represent the multiple factors which may implicitly affect the future scientific collaboration. In this paper we propose an approach based on the multiple heterogeneous network features, which has produced good results in our experiments based on a dataset of more than 30,000 ISI papers. This method can help solving the similar problems of people to people recommendation. It generates high quality expert's profiles via integrating research expertise, co-author network characteristics and researchers' institutional connectivity (local and global) through a SVM-Rank based information merging mechanism to perform intelligent matching. The generated comprehensive profiles alleviate information asymmetry and the multiple similarity measures overcome problems related to information overloading. The proposed method has been implemented in ScholarMate research network (www.scholarmate.com) which is a research 2.0 innovation, promoting research collaboration in virtual scientific community. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/HICSS.2015.73 | HICSS |
Keywords | Field | DocType |
intelligent matching,information asymmetry,researchers institutional connectivity,information overloading,research expertise,scholarmate research network,homogeneous network features extraction,scientific information systems,scientific collaboration,high quality expert profiles,bibliographic systems,recommender systems,pattern matching,virtual scientific community,research collaboration,people to people recommendation,isi papers,social proximity analysis,similarity measures,support vector machine,co-author network characteristics,heterogeneous bibliographic networks,scientific collaborator recommendation,svm-rank based information merging mechanism,support vector machines,feature extraction,topology,network topology,collaboration,semantics | Data science,Recommender system,Information asymmetry,Computer science,Homogeneous,Feature extraction,Network topology,Heterogeneous network,Merge (version control),Semantics | Conference |
ISSN | Citations | PageRank |
1530-1605 | 3 | 0.38 |
References | Authors | |
30 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feng Chen | 1 | 218 | 46.85 |
| Jianshan Sun | 2 | 192 | 17.65 |
| Jian Ma | 3 | 1662 | 103.00 |
| Shanshan Zhang | 4 | 3 | 0.38 |
| Gang Wang | 5 | 139 | 5.82 |
| Zhongsheng Hua | 6 | 740 | 55.13 |