R. Jäschke, L. Marinho, A. Hotho, L. Schmidt-Thieme, and G. Stumme. Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007), page 13-20. Martin-Luther-Universität Halle-Wittenberg, (September 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 present two tag recommendation
algorithms: an adaptation of user-based collaborative
filtering and a graph-based recommender
built on top of FolkRank, an adaptation of the
well-known PageRank algorithm that can cope
with undirected triadic hyperedges. We evaluate
and compare both algorithms on large-scale real
life datasets and show that both provide better
results than non-personalized baseline methods.
Especially the graph-based recommender outperforms
existing methods considerably.
%0 Conference Paper
%1 jaeschke07tagKdml
%A Jäschke, Robert
%A Marinho, Leandro
%A Hotho, Andreas
%A Schmidt-Thieme, Lars
%A Stumme, Gerd
%B Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)
%D 2007
%E Hinneburg, Alexander
%I Martin-Luther-Universität Halle-Wittenberg
%K 2007 folksonomy from:jaeschke kdml l3s lwa myown recommender tagging
%P 13-20
%T Tag Recommendations in Folksonomies
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2007/jaeschke07tagrecommendationsKDML.pdf
%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 present two tag recommendation
algorithms: an adaptation of user-based collaborative
filtering and a graph-based recommender
built on top of FolkRank, an adaptation of the
well-known PageRank algorithm that can cope
with undirected triadic hyperedges. We evaluate
and compare both algorithms on large-scale real
life datasets and show that both provide better
results than non-personalized baseline methods.
Especially the graph-based recommender outperforms
existing methods considerably.
%@ 978-3-86010-907-6
@inproceedings{jaeschke07tagKdml,
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 present two tag recommendation
algorithms: an adaptation of user-based collaborative
filtering and a graph-based recommender
built on top of FolkRank, an adaptation of the
well-known PageRank algorithm that can cope
with undirected triadic hyperedges. We evaluate
and compare both algorithms on large-scale real
life datasets and show that both provide better
results than non-personalized baseline methods.
Especially the graph-based recommender outperforms
existing methods considerably.},
added-at = {2008-01-17T12:54:10.000+0100},
author = {Jäschke, Robert and Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd},
biburl = {https://www.bibsonomy.org/bibtex/2bfc43dfe59f9c0935ac3364b12e6d795/nepomuk},
booktitle = {Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)},
editor = {Hinneburg, Alexander},
interhash = {7e212e3bac146d406035adebff248371},
intrahash = {bfc43dfe59f9c0935ac3364b12e6d795},
isbn = {978-3-86010-907-6},
keywords = {2007 folksonomy from:jaeschke kdml l3s lwa myown recommender tagging},
month = sep,
pages = {13-20},
publisher = {Martin-Luther-Universität Halle-Wittenberg},
timestamp = {2008-01-17T12:54:11.000+0100},
title = {Tag Recommendations in Folksonomies},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2007/jaeschke07tagrecommendationsKDML.pdf},
year = 2007
}