QuME tackles the expertise location and maintenance problem for large-scale Q&A communities. It does so by automatically distributing tasks to people who have the necessary expertise.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. QuME includes novel algorithm to infer expertise levels, making a larger range of social interaction possible. 

Online discussions such as a large-scale community brainstorming often end up with an unorganized bramble of ideas and topics that are difficult to reuse. A process of distillation is needed to boil down a large information space into information that is concise and organized. Arkose is a system-augmented approach to the problem - a set of tools with which human editors can collaboratively distill a large amount of informal information. 

Escalier is a new architecture for community-sourcing configuration settings, and by extention other types of pervasive data such as activity traces. Users can find out what system configurations will be stable in return for giving the community their configuration data. By doing this, both parties get something valuable: End-users can determine whether their system configurations will work, and the community as a whole gets a collaboratively-built datastore, or collective memory.  More>

Our group was one of the originators of expertise sharing as an area of study. Our work has included expertise finders, people recommenders, and collaborative help systems. As well, we have conducted many studies of how people helped one another through computer-mediated communications and within organizations. As the span of networks and computational systems expanded, the scope of our study expanded to Internet-scale systems and communities. Recent work has also expanded to Q&A communities (question-and-answer) communities and social search.