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 2007 of Lecture Notes in Computer Science, page 506--514. Berlin, Heidelberg, Springer, (2007)
DOI: 10.1007/978-3-540-74976-9_52
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.
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 jaeschke-2007
%A Jäschke, Robert
%A 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, Warsaw, Poland, September 17-21, 2007. Proceedings
%C Berlin, Heidelberg
%D 2007
%I Springer
%K p01
%P 506--514
%R 10.1007/978-3-540-74976-9_52
%T Tag Recommendations in Folksonomies
%V 4702 2007
%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
@inproceedings{jaeschke-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 graph-based recommender outperforms existing
methods considerably.},
added-at = {2013-01-10T13:15:31.000+0100},
address = {Berlin, Heidelberg},
author = {Jäschke, Robert and Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd},
biburl = {https://www.bibsonomy.org/bibtex/24a532899b84740a0c5bc800170c06eb8/macek},
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},
doi = {10.1007/978-3-540-74976-9_52},
file = {:jaeschke-2007.pdf:PDF},
interhash = {7e212e3bac146d406035adebff248371},
intrahash = {4a532899b84740a0c5bc800170c06eb8},
isbn = {978-3-540-74975-2},
issn = {0302-9743},
keywords = {p01},
pages = {506--514},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
timestamp = {2013-01-10T13:15:31.000+0100},
title = {Tag Recommendations in Folksonomies},
volume = {4702 2007},
year = 2007
}