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, page 136--149. Berlin/Heidelberg, Springer, (2011)

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.

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http://dx.doi.org/10.1007/978-3-642-22140-8_9

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@inproceedings{illig2009comparison,
  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 = {2011-11-25T12:41:14.000+0100},
  address = {Berlin/Heidelberg},
  author = {Illig, Jens and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
  biburl = {https://puma.uni-kassel.de/bibtex/2f9d6e06ab0f2fdcebb77afa97d72e40a/hotho},
  booktitle = {Knowledge Processing and Data Analysis},
  doi = {10.1007/978-3-642-22140-8_9},
  editor = {Wolff, Karl Erich and Palchunov, Dmitry E. and Zagoruiko, Nikolay G. and Andelfinger, Urs},
  interhash = {cd3420c0f73761453320dc528b3d1e14},
  intrahash = {f9d6e06ab0f2fdcebb77afa97d72e40a},
  isbn = {978-3-642-22139-2},
  keywords = {2011 content folksonomy myown recommendations recommender tag},
  pages = {136--149},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  timestamp = {2012-01-18T10:22:54.000+0100},
  title = {A Comparison of Content-Based Tag Recommendations in Folksonomy Systems},
  url = {http://dx.doi.org/10.1007/978-3-642-22140-8_9},
  vgwort = {23},
  volume = 6581,
  year = 2011
}

%0 Conference Paper
%1 illig2009comparison
%A Illig, Jens
%A Hotho, Andreas
%A Jäschke, Robert
%A Stumme, Gerd
%B Knowledge Processing and Data Analysis
%C Berlin/Heidelberg
%D 2011
%E Wolff, Karl Erich
%E Palchunov, Dmitry E.
%E Zagoruiko, Nikolay G.
%E Andelfinger, Urs
%I Springer
%K 2011 content folksonomy myown recommendations recommender tag
%P 136--149
%R 10.1007/978-3-642-22140-8_9
%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

PUMA

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, page 136--149. Berlin/Heidelberg, Springer, (2011)

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PUMA

A Comparison of Content-Based Tag Recommendations in Folksonomy SystemsJ. Illig, A. Hotho, R. Jäschke, and G. Stumme. Knowledge Processing and Data Analysis, volume 6581 of Lecture Notes in Computer Science, page 136--149. Berlin/Heidelberg, Springer, (2011)

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Cite this publication

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, page 136--149. Berlin/Heidelberg, Springer, (2011)