Publication | Closed Access
QuME
62
Citations
5
References
2007
Year
Unknown Venue
EngineeringCollaborative Information RetrievalOnline CommunitiesSemantic WebCommunity DiscoveryText MiningComputational Social ScienceSocial MediaInformation RetrievalData ScienceSocial SearchSocial Network AnalysisSocial Network DataKnowledge DiscoveryPersonalized SearchHelp-seeking CommunitiesSocial ComputingBusinessSemantic Social Network
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.
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