R. Jäschke, L. 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, Warsaw, Poland, September 17-21, 2007, Proceedings, volume 4702 of Lecture Notes in Computer Science, page 506-514. Springer, (2007)
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 graphbased
recommender outperforms existing methods considerably.
Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, September 17-21, 2007, Proceedings
%0 Conference Paper
%1 Jaschke_2007
%A Jäschke, Robert
%A Marinho, Leandro Balby
%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, Warsaw, Poland, September 17-21, 2007, Proceedings
%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 evaluation folksonomy recommendation tag tagging
%P 506-514
%T Tag Recommendations in Folksonomies
%U http://www.kde.cs.uni-kassel.de/hotho/pub/2007/Tag_Recommender_in_Folksonomies_final.pdf
%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 graphbased
recommender outperforms existing methods considerably.
%@ 978-3-540-74975-2
@inproceedings{Jaschke_2007,
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 graphbased
recommender outperforms existing methods considerably.},
added-at = {2009-07-21T10:00:34.000+0200},
author = {Jäschke, Robert and Marinho, Leandro Balby and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd},
bibsource = {DBLP, http://dblp.uni-trier.de},
biburl = {https://www.bibsonomy.org/bibtex/2b8b87c78e9e27a44aacde0402c642bff/kasimiro},
booktitle = {Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, September 17-21, 2007, Proceedings},
crossref = {DBLP:conf/pkdd/2007},
description = {DBLP Record 'conf/pkdd/JaschkeMHSS07'},
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 = {b8b87c78e9e27a44aacde0402c642bff},
isbn = {978-3-540-74975-2},
keywords = {evaluation folksonomy recommendation tag tagging},
pages = {506-514},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
timestamp = {2010-01-19T14:22:42.000+0100},
title = {Tag Recommendations in Folksonomies},
url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2007/Tag_Recommender_in_Folksonomies_final.pdf},
volume = 4702,
year = 2007
}