Aufsatz in einer Fachzeitschrift
Tag Recommendations in Social Bookmarking Systems



Details zur Publikation
Autor(inn)en:
Jäschke, R.; Hotho, A.; Stumme, G.
Herausgeber:
Giunchiglia Enrico
Verlag:
IOS Press
Verlagsort / Veröffentlichungsort:
Amsterdam
Publikationsjahr:
2008
Zeitschrift:
AI Communications
Seitenbereich:
231-247
Jahrgang/Band:
21
ISSN:
0921-7126

Zusammenfassung, 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 several recommendation algorithms on large-scale real life datasets: an adaptation ofuser-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 ap


Schlagwörter
recommender, tag, top, webzu


Autor(inn)en / Herausgeber(innen)

Zuletzt aktualisiert 2019-01-11 um 16:06