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 large-scale 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.
%0 Book Section
%1 jaeschke2007recommendations
%A Jäschke, Robert
%A Marinho, Leandro
%A Hotho, Andreas
%A Schmidt-Thieme, Lars
%A Stumme, Gerd
%B Knowledge Discovery in Databases: PKDD 2007
%C Berlin / Heidelberg
%D 2007
%E Kok, Joost
%E Koronacki, Jacek
%E Lopez de Mantaras, Ramon
%E Matwin, Stan
%E Mladenic, Dunja
%E Skowron, Andrzej
%I Springer
%K folksonomy recommendation tagging
%P 506-514
%R 10.1007/978-3-540-74976-9_52
%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 large-scale 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
@incollection{jaeschke2007recommendations,
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 large-scale 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 = {2011-12-05T07:58:27.000+0100},
address = {Berlin / Heidelberg},
affiliation = {Knowledge & Data Engineering Group (KDE), University of Kassel, Wilhelmshöher Allee 73, 34121 Kassel Germany},
author = {Jäschke, Robert and Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd},
biburl = {https://www.bibsonomy.org/bibtex/24704bbf1267f873d9e53afceaf097f22/beate},
booktitle = {Knowledge Discovery in Databases: PKDD 2007},
doi = {10.1007/978-3-540-74976-9_52},
editor = {Kok, Joost and Koronacki, Jacek and Lopez de Mantaras, Ramon and Matwin, Stan and Mladenic, Dunja and Skowron, Andrzej},
interhash = {7e212e3bac146d406035adebff248371},
intrahash = {4704bbf1267f873d9e53afceaf097f22},
isbn = {978-3-540-74975-2},
keyword = {Computer Science},
keywords = {folksonomy recommendation tagging},
pages = {506-514},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
timestamp = {2011-12-05T07:58:27.000+0100},
title = {Tag Recommendations in Folksonomies},
url = {http://dx.doi.org/10.1007/978-3-540-74976-9_52},
volume = 4702,
year = 2007
}