<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhang, Jun</style></author><author><style face="normal" font="default" size="100%">Mark S. Ackerman</style></author><author><style face="normal" font="default" size="100%">Lada A. 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 20th Annual ACM Symposium on User Interface Software and Technology (UIST&#039;07)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">cscw</style></keyword><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%">social networks</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2007</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">Complete</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">111–114</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&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&#039; 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&#039;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;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhang, Jun</style></author><author><style face="normal" font="default" size="100%">Mark S. Ackerman</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Searching for Expertise in Social Networks: A Simulation of Potential Strategies</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 2005 International ACM SIGGROUP Conference on Supporting Group Work (Group&#039;05)</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%">expertise sharing</style></keyword><keyword><style  face="normal" font="default" size="100%">information seeking</style></keyword><keyword><style  face="normal" font="default" size="100%">organizational simulations</style></keyword><keyword><style  face="normal" font="default" size="100%">social computing</style></keyword><keyword><style  face="normal" font="default" size="100%">social networks</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">Complete</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">71–80</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;People search for people with suitable expertise all of the time in their social networks - to answer questions or provide help. Recently, efforts have been made to augment this searching. However, relatively little is known about the social characteristics of various algorithms that might be useful. In this paper, we examine three families of searching strategies that we believe may be useful in expertise location. We do so through a simulation, based on the Enron email data set. (We would be unable to suitably experiment in a real organization, thus our need for a simulation.) Our emphasis is not on graph theoretical concerns, but on the social characteristics involved. The goal is to understand the tradeoffs involved in the design of social network based searching engines.&lt;br&gt;&amp;nbsp;&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">McDonald, David W.</style></author><author><style face="normal" font="default" size="100%">Mark S. Ackerman</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Expertise Recommender: A Flexible Recommendation System and Architecture</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work (CSCW&#039;00)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">collaborative filtering</style></keyword><keyword><style  face="normal" font="default" size="100%">expert locators</style></keyword><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%">expertise sharing</style></keyword><keyword><style  face="normal" font="default" size="100%">information seeking</style></keyword><keyword><style  face="normal" font="default" size="100%">recommendation systems</style></keyword><keyword><style  face="normal" font="default" size="100%">software architecture</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">Complete</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">231–240</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">McDonald, David W.</style></author><author><style face="normal" font="default" size="100%">Mark S. Ackerman</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Just Talk to Me: A Field Study of Expertise Location</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 1998 ACM Conference on Computer Supported Cooperative Work (CSCW&#039;98)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">bug reporting</style></keyword><keyword><style  face="normal" font="default" size="100%">CMC</style></keyword><keyword><style  face="normal" font="default" size="100%">computer mediated communications</style></keyword><keyword><style  face="normal" font="default" size="100%">expert locators</style></keyword><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%">expertise networks</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise sharing</style></keyword><keyword><style  face="normal" font="default" size="100%">information seeking</style></keyword><keyword><style  face="normal" font="default" size="100%">knowledge networks</style></keyword><keyword><style  face="normal" font="default" size="100%">knowledge sharing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1998</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/1998</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">Complete</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">315–324</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;</style></abstract></record></records></xml>