%0 Conference Paper %1 jaeschke07tagKdml %A Jäschke, Robert %A Marinho, Leandro %A Hotho, Andreas %A Schmidt-Thieme, Lars %A Stumme, Gerd %B Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007) %D 2007 %E Hinneburg, Alexander %I Martin-Luther-Universität Halle-Wittenberg %K 2007 folksonomy kdml l3s lwa myown recommender tagging %P 13-20 %T Tag Recommendations in Folksonomies %U http://www.kde.cs.uni-kassel.de/stumme/papers/2007/jaeschke07tagrecommendationsKDML.pdf %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 present two tag recommendation algorithms: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank, an adaptation of the well-known PageRank algorithm that can cope with undirected triadic hyperedges. We evaluate and compare both algorithms on large-scale real life datasets and show that both provide better results than non-personalized baseline methods. Especially the graph-based recommender outperforms existing methods considerably. %@ 978-3-86010-907-6

@inproceedings{jaeschke07tagKdml, 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 present two tag recommendation algorithms: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank, an adaptation of the well-known PageRank algorithm that can cope with undirected triadic hyperedges. We evaluate and compare both algorithms on large-scale real life datasets and show that both provide better results than non-personalized baseline methods. Especially the graph-based recommender outperforms existing methods considerably.}, added-at = {2008-01-17T13:52:39.000+0100}, author = {Jäschke, Robert and Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd}, biburl = {https://www.bibsonomy.org/bibtex/2bfc43dfe59f9c0935ac3364b12e6d795/jaeschke}, booktitle = {Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)}, editor = {Hinneburg, Alexander}, interhash = {7e212e3bac146d406035adebff248371}, intrahash = {bfc43dfe59f9c0935ac3364b12e6d795}, isbn = {978-3-86010-907-6}, keywords = {2007 folksonomy kdml l3s lwa myown recommender tagging}, month = sep, pages = {13-20}, publisher = {Martin-Luther-Universität Halle-Wittenberg}, timestamp = {2014-07-28T15:57:31.000+0200}, title = {Tag Recommendations in Folksonomies}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2007/jaeschke07tagrecommendationsKDML.pdf}, vgwort = {20}, year = 2007 }