Tag Recommendations in Social Bookmarking Systems.
AI Communications, 21(4):231-247, 2008.
Robert Jäschke, Leandro Marinho, Andreas Hotho, Lars Schmidt-Thieme and Gerd Stumme.
[doi]
[abstract]
[BibTeX]
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.
Tag Recommendations in Folksonomies.
In: J. N. Kok, J. Koronacki, R. L. de Mántaras, S. Matwin, D. Mladenic and A. Skowron, editors,
Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, volume 4702, series Lecture Notes in Computer Science, pages 506-514.
Springer, Berlin, Heidelberg, 2007.
Robert Jäschke, Leandro Balby Marinho, Andreas Hotho, Lars Schmidt-Thieme and Gerd Stumme.
[doi]
[abstract]
[BibTeX]
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.