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Dynamic context-sensitive pagerank for expertise mining

By Daniel Schall and Schahram Dustdar


Abstract. Online tools for collaboration and social platforms have become omnipresent in Web-based environments. Interests and skills of people evolve over time depending in performed activities and joint collaborations. We believe that ranking models for recommending experts or collaboration partners should not only rely on profiles or skill information that need to be manually maintained and updated by the user. In this work we address the problem of expertise mining based on performed interactions between people. We argue that an expertise mining algorithm must consider a person’s interest and activity level in a certain collaboration context. Our approach is based on the PageRank algorithm enhanced by techniques to incorporate contextual link information. An approach comprising two steps is presented. First, offline analysis of human interactions considering tagged interaction links and second composition of ranking scores based on preferences. We evaluate our approach using an email interaction network.

Publisher: Springer
Year: 2010
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
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