Tag Recommendations in Folksonomies
R. Jäschke, L. Balby Marinho, A. Hotho, L. Schmidt-Thieme, and G. Stumme.
Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, volume 4702 of Lecture Notes in Computer Science, page 506-514. Berlin, Heidelberg, Springer, (2007)

Collaborative tagging systems allow users to assign keywords—so called “tags”—to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare two recommendation algorithms on largescale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank. We show that both provide better results than non-personalized baseline methods. Especially the graph-based recommender outperforms existing methods considerably.

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http://dx.doi.org/10.1007/978-3-540-74976-9_52

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@inproceedings{jaeschke2007tag,
  abstract = {Collaborative tagging systems allow users to assign keywords—so called “tags”—to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied.
In this paper we evaluate and compare two recommendation algorithms on largescale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank. We show that both provide better results than non-personalized baseline methods. Especially the graph-based recommender outperforms existing methods considerably.},
  added-at = {2012-10-09T17:53:14.000+0200},
  address = {Berlin, Heidelberg},
  author = {Jäschke, Robert and Balby Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd},
  biburl = {https://puma.uni-kassel.de/bibtex/2bb8ecec699a2f129322fe334747c6aef/stephandoerfel},
  booktitle = {Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases},
  editor = {Kok, Joost N. and Koronacki, Jacek and de Mántaras, Ramon López and Matwin, Stan and Mladenic, Dunja and Skowron, Andrzej},
  ee = {http://dx.doi.org/10.1007/978-3-540-74976-9_52},
  interhash = {7e212e3bac146d406035adebff248371},
  intrahash = {bb8ecec699a2f129322fe334747c6aef},
  isbn = {978-3-540-74975-2},
  keywords = {folkrank recommendations},
  pages = {506-514},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  timestamp = {2012-10-09T17:53:14.000+0200},
  title = {Tag Recommendations in Folksonomies},
  url = {http://dx.doi.org/10.1007/978-3-540-74976-9_52},
  vgwort = {14},
  volume = 4702,
  year = 2007
}

%0 Conference Paper
%1 jaeschke2007tag
%A Jäschke, Robert
%A Balby Marinho, Leandro
%A Hotho, Andreas
%A Schmidt-Thieme, Lars
%A Stumme, Gerd
%B Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases
%C Berlin, Heidelberg
%D 2007
%E Kok, Joost N.
%E Koronacki, Jacek
%E de Mántaras, Ramon López
%E Matwin, Stan
%E Mladenic, Dunja
%E Skowron, Andrzej
%I Springer
%K folkrank recommendations
%P 506-514
%T Tag Recommendations in Folksonomies
%U http://dx.doi.org/10.1007/978-3-540-74976-9_52
%V 4702
%X Collaborative tagging systems allow users to assign keywords—so called “tags”—to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied.
In this paper we evaluate and compare two recommendation algorithms on largescale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank. We show that both provide better results than non-personalized baseline methods. Especially the graph-based recommender outperforms existing methods considerably.
%@ 978-3-540-74975-2

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