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.
%0 Conference Paper
%1 citeulike:2813437
%A Zhang, Jun
%A Ackerman, Mark S.
%A Adamic, Lada
%A Nam, Kevin K.
%B UIST '07: Proceedings of the 20th annual ACM symposium on User interface software and technology
%C New York, NY, USA
%D 2007
%I ACM
%K expert-finding social-network
%P 111--114
%R 10.1145/1294211.1294230
%T QuME: a mechanism to support expertise finding in online help-seeking communities
%U http://dx.doi.org/10.1145/1294211.1294230
%X 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.
%@ 978-1-59593-679-2
@inproceedings{citeulike:2813437,
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.}},
added-at = {2018-03-19T12:24:51.000+0100},
address = {New York, NY, USA},
author = {Zhang, Jun and Ackerman, Mark S. and Adamic, Lada and Nam, Kevin K.},
biburl = {https://www.bibsonomy.org/bibtex/2cdd420f2aaff4bfcdff44bdc2a9dc0da/aho},
booktitle = {UIST '07: Proceedings of the 20th annual ACM symposium on User interface software and technology},
citeulike-article-id = {2813437},
citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1294230},
citeulike-linkout-1 = {http://dx.doi.org/10.1145/1294211.1294230},
doi = {10.1145/1294211.1294230},
interhash = {5c1b228ef26ed62e9213f08c758742e0},
intrahash = {cdd420f2aaff4bfcdff44bdc2a9dc0da},
isbn = {978-1-59593-679-2},
keywords = {expert-finding social-network},
location = {Newport, Rhode Island, USA},
pages = {111--114},
posted-at = {2009-06-30 10:38:42},
priority = {2},
publisher = {ACM},
timestamp = {2018-03-19T12:24:51.000+0100},
title = {{QuME: a mechanism to support expertise finding in online help-seeking communities}},
url = {http://dx.doi.org/10.1145/1294211.1294230},
year = 2007
}