A Probabilistic Ranking Approach for Tag Recommendation
Z. Liao, M. Xie, H. Cao, and Y. Huang. ECML PKDD Discovery Challenge 2009 (DC09), 497, page 143--155. Bled, Slovenia, CEUR Workshop Proceedings, (September 2009)
Abstract
Social Tagging is a typical Web 2.0 application for users to share knowledge and organize the massive web resources. Choosing appropriate words as tags might be time consuming for users, thus a tag recommendation system is needed for accelerating this procedure. In this paper we formulate tag recommendation as a probabilistic ranking process, especially we propose a hybrid probabilistic approach which combines language model and statistical machine translation model. Experimental results validate the effectiveness of our method.
%0 Conference Paper
%1 marinho:ecml2009
%A Liao, Zhen
%A Xie, Maoqiang
%A Cao, Hao
%A Huang, Yalou
%B ECML PKDD Discovery Challenge 2009 (DC09)
%C Bled, Slovenia
%D 2009
%E Eisterlehner, Folke
%E Hotho, Andreas
%E Jäschke, Robert
%I CEUR Workshop Proceedings
%K 2009 ECML09 _todo recommendation tagging
%P 143--155
%T A Probabilistic Ranking Approach for Tag Recommendation
%U http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-497/
%V 497
%X Social Tagging is a typical Web 2.0 application for users to share knowledge and organize the massive web resources. Choosing appropriate words as tags might be time consuming for users, thus a tag recommendation system is needed for accelerating this procedure. In this paper we formulate tag recommendation as a probabilistic ranking process, especially we propose a hybrid probabilistic approach which combines language model and statistical machine translation model. Experimental results validate the effectiveness of our method.
@inproceedings{marinho:ecml2009,
abstract = {Social Tagging is a typical Web 2.0 application for users to share knowledge and organize the massive web resources. Choosing appropriate words as tags might be time consuming for users, thus a tag recommendation system is needed for accelerating this procedure. In this paper we formulate tag recommendation as a probabilistic ranking process, especially we propose a hybrid probabilistic approach which combines language model and statistical machine translation model. Experimental results validate the effectiveness of our method.},
added-at = {2010-01-29T16:32:47.000+0100},
address = {Bled, Slovenia},
author = {Liao, Zhen and Xie, Maoqiang and Cao, Hao and Huang, Yalou},
biburl = {https://www.bibsonomy.org/bibtex/2e17e02119eb2c01466f626700495817a/trude},
booktitle = {ECML PKDD Discovery Challenge 2009 (DC09)},
editor = {Eisterlehner, Folke and Hotho, Andreas and Jäschke, Robert},
interhash = {1ae37f968a362376d281a8005fd4a1c0},
intrahash = {e17e02119eb2c01466f626700495817a},
issn = {1613-0073},
keywords = {2009 ECML09 _todo recommendation tagging},
month = {September},
pages = {143--155},
publisher = {CEUR Workshop Proceedings},
timestamp = {2010-01-29T16:32:47.000+0100},
title = {A Probabilistic Ranking Approach for Tag Recommendation},
url = {http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-497/},
volume = 497,
year = 2009
}