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The authors introduce a link intensity-based ranking model for recommending relevant users in human interaction networks. In open, dynamic collaboration environments enabled by Service-Oriented Architecture (SOA), it is ever more important to determine the expertise and skills of users in an automated manner. Additionally, a ranking model for humans must consider metrics such as availability, activity level, and expected informedness of users. They present DSARank for estimating the relative importance of users based on the concept of eigenvector centrality in collaboration networks.
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