Illig, J.; Hotho, A.; Jäschke, R. & Stumme, G. (2011),
A Comparison of content-based Tag Recommendations in Folksonomy Systems, in
'Postproceedings of the International Conference on Knowledge Processing in Practice (KPP 2007)'
, Springer,
.
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[Kurzfassung]
[BibTeX]
[Endnote]
Jäschke, R.; Marinho, L.; Hotho, A.; Schmidt-Thieme, L. & Stumme, G. (2008),
'Tag Recommendations in Social Bookmarking Systems', AI Communications
21
(4)
, 231-247
.
[Volltext]
[Kurzfassung]
[BibTeX]
[Endnote]
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 several recommendation algorithms on large-scale real life datasets: an adaptation of
user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.
Jäschke, R.; Marinho, L. B.; Hotho, A.; Schmidt-Thieme, L. & Stumme, G. (2007),
Tag Recommendations in Folksonomies, in
Joost N. Kok; Jacek Koronacki; Ramon López de Mántaras; Stan Matwin; Dunja Mladenic & Andrzej Skowron, ed.,
'Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases'
, Springer, Berlin, Heidelberg
, pp. 506-514
.
[Volltext]
[Kurzfassung]
[BibTeX]
[Endnote]
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.