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.
ER - TY - CONF AU - Jäschke, Robert AU - Marinho, Leandro Balby AU - Hotho, Andreas AU - Schmidt-Thieme, Lars AU - Stumme, Gerd A2 - Kok, Joost N. A2 - Koronacki, Jacek A2 - de Mántaras, Ramon López A2 - Matwin, Stan A2 - Mladenic, Dunja A2 - Skowron, Andrzej T1 - Tag Recommendations in Folksonomies T2 - Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases PB - Springer CY - Berlin, Heidelberg PY - 2007/ M2 - VL - 4702 IS - SP - 506 EP - 514 UR - http://dx.doi.org/10.1007/978-3-540-74976-9_52 M3 - KW - 2007 KW - FolkRank KW - Folksonomies KW - Recommendations KW - folksonomies KW - itegpub KW - l3s KW - myown KW - nepomuk KW - ranking KW - recommendations KW - tagging L1 - SN - 978-3-540-74975-2 N1 - N1 - AB - 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. ER -