A Comparison of Content-Based Tag Recommendations in Folksonomy Systems
J. Illig, A. Hotho, R. Jäschke, and G. Stumme. Knowledge Processing and Data Analysis, volume 6581 of Lecture Notes in Computer Science, Springer Berlin / Heidelberg, 10.1007/978-3-642-22140-8_9.(2011)
Abstract
Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset.
Knowledge & Data Engineering Group, Department of Mathematics and Computer Science, University of Kassel, Wilhelmshöher Allee 73, 34121 Kassel, Germany
%0 Book Section
%1 illig2011comparison
%A Illig, Jens
%A Hotho, Andreas
%A Jäschke, Robert
%A Stumme, Gerd
%B Knowledge Processing and Data Analysis
%D 2011
%E Wolff, Karl
%E Palchunov, Dmitry
%E Zagoruiko, Nikolay
%E Andelfinger, Urs
%I Springer Berlin / Heidelberg
%K 2011 based comparison content diplom diplomarbeit folksonomy illig info20 itegpub jens journal kde l3s mykopie myown own publication recommendation recommendations recommender stumme tag taggingsurvey tagorapub
%P 136-149
%T A Comparison of Content-Based Tag Recommendations in Folksonomy Systems
%U http://dx.doi.org/10.1007/978-3-642-22140-8_9
%V 6581
%X Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset.
%@ 978-3-642-22139-2
@incollection{illig2011comparison,
abstract = {Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset. },
added-at = {2015-04-14T13:51:41.000+0200},
affiliation = {Knowledge & Data Engineering Group, Department of Mathematics and Computer Science, University of Kassel, Wilhelmshöher Allee 73, 34121 Kassel, Germany},
author = {Illig, Jens and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
biburl = {https://www.bibsonomy.org/bibtex/2f5513e0daea5dd5dd5e64821f23c7577/kdepublication},
booktitle = {Knowledge Processing and Data Analysis},
editor = {Wolff, Karl and Palchunov, Dmitry and Zagoruiko, Nikolay and Andelfinger, Urs},
interhash = {cd3420c0f73761453320dc528b3d1e14},
intrahash = {f5513e0daea5dd5dd5e64821f23c7577},
isbn = {978-3-642-22139-2},
keyword = {Computer Science},
keywords = {2011 based comparison content diplom diplomarbeit folksonomy illig info20 itegpub jens journal kde l3s mykopie myown own publication recommendation recommendations recommender stumme tag taggingsurvey tagorapub},
note = {10.1007/978-3-642-22140-8_9},
pages = {136-149},
publisher = {Springer Berlin / Heidelberg},
series = {Lecture Notes in Computer Science},
timestamp = {2015-04-24T14:46:18.000+0200},
title = {A Comparison of Content-Based Tag Recommendations in Folksonomy Systems},
url = {http://dx.doi.org/10.1007/978-3-642-22140-8_9},
volume = 6581,
year = 2011
}