DocumentCode :
2188066
Title :
Scientific Collaborator Recommendation in Heterogeneous Bibliographic Networks
Author :
Chen Yang ; Jianshan Sun ; Jian Ma ; Shanshan Zhang ; Gang Wang ; Zhongsheng Hua
fYear :
2015
fDate :
5-8 Jan. 2015
Firstpage :
552
Lastpage :
561
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.
Keywords :
bibliographic systems; pattern matching; recommender systems; scientific information systems; support vector machines; ISI papers; SVM-rank based information merging mechanism; ScholarMate research network; co-author network characteristics; heterogeneous bibliographic networks; high quality expert profiles; homogeneous network features extraction; information asymmetry; information overloading; intelligent matching; people to people recommendation; research collaboration; research expertise; researchers institutional connectivity; scientific collaboration; scientific collaborator recommendation; similarity measures; social proximity analysis; support vector machine; virtual scientific community; Collaboration; Feature extraction; Network topology; Recommender systems; Semantics; Social network services; Topology;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
System Sciences (HICSS), 2015 48th Hawaii International Conference on
Conference_Location :
Kauai, HI
ISSN :
1530-1605
Type :
conf
DOI :
10.1109/HICSS.2015.73
Filename :
7069722
Link To Document :
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