BibSonomy publications for /user/jaeschke/recommenderThu Jan 17 13:52:39 CET 2008Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)sep13-20Tag Recommendations in Folksonomies20072007 myown tagging folksonomy l3s kdml recommender lwa for:nepomuk 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.Thu Jan 17 13:51:14 CET 2008Berlin, 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 Folksonomies47022007tagging wp5 for:nepomuk myown l3s recommender folksonomy 2007 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.Fri Nov 16 11:49:53 CET 2007Frontiers of WWW Research and Development - APWeb 2006733--738Cubic Analysis of Social Bookmarking for Personalized Recommendation2006recommender folksonomy Personalized recommendation is used to conquer the information overload problem, and collaborative filtering recommendation (CF) is one of the most successful recommendation techniques to date. However, CF becomes less effective when users have multiple interests, because users have similar taste in one aspect may behave quite different in other aspects. Information got from social bookmarking websites not only tells what a user likes, but also why he or she likes it. This paper proposes a division algorithm and a CubeSVD algorithm to analysis this information, distill the interrelations between different users’ various interests, and make better personalized recommendation based on them. Experiment reveals the superiority of our method over traditional CF methods. ER -Mon Aug 20 10:49:20 CEST 2007User Modeling and User-Adapted Interaction4331-370Hybrid Recommender Systems: Survey and Experiments.122002survey cf recommender Wed May 02 18:13:06 CEST 2007New York, NY, USAACM Trans. Inf. Syst.15--53Evaluating collaborative filtering recommender systems222004evaluation collaborative recommender filtering Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.Wed Apr 19 16:11:52 CEST 2006Proceedings of the 10th International WWW Conference285-295Item-based collaborative filtering recommendation algorithms2001collaborative filtering recommender