@inproceedings{e7ae84ea3b7b442aa1af1d9df798999c,
title = "CollabSeer: A search engine for collaboration discovery",
abstract = "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 CollabSeer, 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 analysis 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 a subset of the CiteSeerX database show that CollabSeer can efficiently discover prospective collaborators.",
keywords = "coauthor network, digital library, graph theory, information retrieval, link analysis, social network",
author = "Chen, {Hung Hsuan} and Liang Gou and Xiaolong Zhang and Giles, {Clyde Lee}",
year = "2011",
doi = "10.1145/1998076.1998121",
language = "???core.languages.en_GB???",
isbn = "9781450307444",
series = "Proceedings of the ACM/IEEE Joint Conference on Digital Libraries",
pages = "231--240",
booktitle = "JCDL'11 - Proceedings of the 2011 ACM/IEEE Joint Conference on Digital Libraries",
note = "11th Annual International ACM/IEEE Joint Conference on Digital Libraries, JCDL'11 ; Conference date: 13-06-2011 Through 17-06-2011",
}