TY - CONF T1 - Expertise networks in online communities: structure and algorithms T2 - Proceedings of the 16th international conference on World Wide Web (WWW'07) Y1 - 2007 A1 - Zhang, Jun A1 - Mark S. Ackerman A1 - Lada A. Adamic KW - expert locators KW - expertise finding KW - help seeking KW - online communities KW - simulation KW - social network analysis AB -

Web-based communities have become important places for people to seek and share expertise. We find that networks in these communities typically differ in their topology from other online networks such as the World Wide Web. Systems targeted to augment web-based communities by automatically identifying users with expertise, for example, need to adapt to the underlying interaction dynamics. In this study, we analyze the Java Forum, a large online help-seeking community, using social network analysis methods. We test a set of network-based ranking algorithms, including PageRank and HITS, on this large size social network in order to identify users with high expertise. We then use simulations to identify a small number of simple simulation rules governing the question-answer dynamic in the network. These simple rules not only replicate the structural characteristics and algorithm performance on the empirically observed Java Forum, but also allow us to evaluate how other algorithms may perform in communities with different characteristics. We believe this approach will be fruitful for practical algorithm design and implementation for online expertise-sharing communities.
 

JF - Proceedings of the 16th international conference on World Wide Web (WWW'07) PB - ACM UR - Complete ER - TY - CONF T1 - Expertise Recommender: A Flexible Recommendation System and Architecture T2 - Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work (CSCW'00) Y1 - 2000 A1 - McDonald, David W. A1 - Mark S. Ackerman KW - collaborative filtering KW - expert locators KW - expertise finding KW - expertise location KW - expertise sharing KW - information seeking KW - recommendation systems KW - software architecture AB -

Locating the expertise necessary to solve difficult problems is a nuanced social and collaborative problem. In organizations, some people assist others in locating expertise by making referrals. People who make referrals fill key organizational roles that have been identified by CSCW and affiliated research. Expertise locating systems are not designed to replace people who fill these key organizational roles. Instead, expertise locating systems attempt to decrease workload and support people who have no other options. Recommendation systems are collaborative software that can be applied to expertise locating. This work describes a general recommendation architecture that is grounded in a field study of expertise locating. Our expertise recommendation system details the work necessary to fit expertise recommendation to a work setting. The architecture and implementation begin to tease apart the technical aspects of providing good recommendations from social and collaborative concerns.

JF - Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work (CSCW'00) UR - Complete ER - TY - CONF T1 - Just Talk to Me: A Field Study of Expertise Location T2 - Proceedings of the 1998 ACM Conference on Computer Supported Cooperative Work (CSCW'98) Y1 - 1998 A1 - McDonald, David W. A1 - Mark S. Ackerman KW - bug reporting KW - CMC KW - computer mediated communications KW - expert locators KW - expertise finding KW - expertise location KW - expertise networks KW - expertise sharing KW - information seeking KW - knowledge networks KW - knowledge sharing AB -

Everyday, people in organizations must solve their problems to get their work accomplished. To do so, they often must find others with knowledge and information. Systems that assist users with finding such expertise are increasingly interesting to organizations and scientific communities. But, as we begin to design and construct such systems, it is important to determine what we are attempting to augment. Accordingly, we conducted a five-month field study of a medium-sized software firm. We found the participants use complex, iterative behaviors to minimize the number of possible expertise sources, while at the same time, provide a high possibility of garnering the necessary expertise. We briefly consider the design implications of the identification, selection, and escalation behaviors found during our field study.

JF - Proceedings of the 1998 ACM Conference on Computer Supported Cooperative Work (CSCW'98) UR - Complete ER -