A. Budura, D. Bourges-Waldegg, и J. Riordan. CSE '09: Proceedings of the 2009 International Conference on Computational Science and Engineering, стр. 34--41. Washington, DC, USA, IEEE Computer Society, (2009)
DOI: 10.1109/CSE.2009.404
Аннотация
We propose a novel approach to the problem of expertise mining in an enterprise, taking advantage of online social applications deployed within the enterprise. Based on the assumption that the users’ interactions with such social software reflect to some extent their expertise, we devise a probabilistic method for identifying the main areas of expertise of users based solely on their set of tags extracted from a social bookmarking system. We base our approach on statistical language models, which we adapt to fit our unique setting. We train and validate our model on a real world dataset extracted from two IBM-internal applications. Our results show that our approach provides a viable alternative to other methods that rely on documents extracted from the enterprise corpora.
%0 Conference Paper
%1 1633735
%A Budura, Adriana
%A Bourges-Waldegg, Daniela
%A Riordan, James
%B CSE '09: Proceedings of the 2009 International Conference on Computational Science and Engineering
%C Washington, DC, USA
%D 2009
%I IEEE Computer Society
%K expertise profiles tagging
%P 34--41
%R 10.1109/CSE.2009.404
%T Deriving Expertise Profiles from Tags
%U http://portal.acm.org/citation.cfm?id=1632710.1633735
%X We propose a novel approach to the problem of expertise mining in an enterprise, taking advantage of online social applications deployed within the enterprise. Based on the assumption that the users’ interactions with such social software reflect to some extent their expertise, we devise a probabilistic method for identifying the main areas of expertise of users based solely on their set of tags extracted from a social bookmarking system. We base our approach on statistical language models, which we adapt to fit our unique setting. We train and validate our model on a real world dataset extracted from two IBM-internal applications. Our results show that our approach provides a viable alternative to other methods that rely on documents extracted from the enterprise corpora.
%@ 978-0-7695-3823-5
@inproceedings{1633735,
abstract = {We propose a novel approach to the problem of expertise mining in an enterprise, taking advantage of online social applications deployed within the enterprise. Based on the assumption that the users’ interactions with such social software reflect to some extent their expertise, we devise a probabilistic method for identifying the main areas of expertise of users based solely on their set of tags extracted from a social bookmarking system. We base our approach on statistical language models, which we adapt to fit our unique setting. We train and validate our model on a real world dataset extracted from two IBM-internal applications. Our results show that our approach provides a viable alternative to other methods that rely on documents extracted from the enterprise corpora.},
added-at = {2010-05-11T20:29:27.000+0200},
address = {Washington, DC, USA},
author = {Budura, Adriana and Bourges-Waldegg, Daniela and Riordan, James},
biburl = {https://www.bibsonomy.org/bibtex/2c03eb91d7cf39682012c7f56d5e997ee/kasimiro},
booktitle = {CSE '09: Proceedings of the 2009 International Conference on Computational Science and Engineering},
description = {Deriving Expertise Profiles from Tags},
doi = {10.1109/CSE.2009.404},
interhash = {676a4c1ddc5b737d15a1ed71aaae5d7c},
intrahash = {c03eb91d7cf39682012c7f56d5e997ee},
isbn = {978-0-7695-3823-5},
keywords = {expertise profiles tagging},
pages = {34--41},
publisher = {IEEE Computer Society},
timestamp = {2010-05-11T20:29:27.000+0200},
title = {Deriving Expertise Profiles from Tags},
url = {http://portal.acm.org/citation.cfm?id=1632710.1633735},
year = 2009
}