BibSonomyburst/user/nepomuk/recommenderBibSonomy RSS feed for /user/nepomuk/recommender2012-02-16T20:56:43+01:00http://www.bibsonomy.org/bibtex/2bfc43dfe59f9c0935ac3364b12e6d795/nepomuknepomuk2008-01-17T12:54:10+01:002007 folksonomy from:jaeschke kdml l3s lwa myown recommender tagging <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Marinho">Leandro Marinho</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Schmidt-Thieme">Lars Schmidt-Thieme</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität LWA 2007, </em><em>page 13-20. </em><em>Martin-Luther-Universität Halle-Wittenberg, </em>(<em>September 2007</em>)Thu Jan 17 12:54:10 CET 2008Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)sep13-20Tag Recommendations in Folksonomies20072007 folksonomy from:jaeschke kdml l3s lwa myown recommender tagging 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.http://www.bibsonomy.org/bibtex/2bb8ecec699a2f129322fe334747c6aef/nepomuknepomuk2007-11-12T12:55:47+01:002007 folksonomy from:jaeschke l3s myown recommender tagging wp5 <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Marinho">Leandro Balby Marinho</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Schmidt-Thieme">Lars Schmidt-Thieme</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, </em><em>volume 4702 of Lecture Notes in Computer Science, </em><em>page 506-514. </em><em>Berlin, Heidelberg, </em><em>Springer, </em>(<em>2007</em>)Mon Nov 12 12:55:47 CET 2007Berlin, HeidelbergKnowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases506-514Lecture Notes in Computer ScienceTag Recommendations in Folksonomies470220072007 folksonomy from:jaeschke l3s myown recommender tagging wp5 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.