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Research Project
Web 2.0 Community Data Mining and Expertise (QuME)
Expertise finders are an important class of collaborative recommendation systems, but they suffer from a general problem: Current expertise finders, both commercial and research, cannot infer expertise levels very well. Traditionally, expertise finders have relied on the standard information similarity measures (such as term vector comparisons). The ability to add the level of expertise would be a major step forward for expertise finders, and would likely open up a range of new application possibilities. This work proposes to solve this problem by constructing a prototype middleware system, called QuME, which contains a number of mechanisms to facilitate expertise finding, expertise exchange, and social interaction for online communities and organizations. QuME includes novel mechanisms to infer expertise levels, making a larger range of social interaction possible.
Contact: Mark Ackerman (ackerm@umich.edu)
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