QuME: a mechanism to support expertise finding in online help-seeking communities

  • Authors:
  • Jun Zhang;Mark S. Ackerman;Lada Adamic;Kevin Kyung Nam

  • Affiliations:
  • University of Michigan, Ann Arbor, MI;University of Michigan, Ann Arbor, MI;University of Michigan, Ann Arbor, MI;University of Michigan, Ann Arbor, MI

  • Venue:
  • Proceedings of the 20th annual ACM symposium on User interface software and technology
  • Year:
  • 2007

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Abstract

Help-seeking communities have been playing an increasingly critical role in the way people seek and share information. However, traditional help-seeking mechanisms of these online communities have some limitations. In this paper, we describe an expertise-finding mechanism that attempts to alleviate the limitations caused by not knowing users' expertise levels. As a result of using social network data from the online community, this mechanism can automatically infer expertise level. This allows, for example, a question list to be personalized to the user's expertise level as well as to keyword similarity. We believe this expertise location mechanism will facilitate the development of next generation help-seeking communities.