<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jun Zhang</style></author><author><style face="normal" font="default" size="100%">Mark S. Ackerman</style></author><author><style face="normal" font="default" size="100%">Lada Adamic</style></author><author><style face="normal" font="default" size="100%">Kevin K. Nam</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">QuME: A Mechanism to Support Expertise Finding in Online Help-seeking Communities</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the ACM Symposium on User Interface Systems and Technology (UIST'07)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">expertise finding</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise location</style></keyword><keyword><style  face="normal" font="default" size="100%">online communities</style></keyword><keyword><style  face="normal" font="default" size="100%">QuME</style></keyword><keyword><style  face="normal" font="default" size="100%">social computing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.eecs.umich.edu/~ackerm/pub/07b43/zhangackermanadamicnam.uist07.final-a.pdf</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">111-114</style></pages><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;span style=&quot;font-size: 10pt&quot;&gt;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&amp;rsquo; expertise levels. As a result of using social network data from the online community, this mechanism can automatically infer expertise level.&amp;nbsp;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&lt;/span&gt;&lt;/p&gt;</style></abstract></record></records></xml>
