http://www.bibsonomy.org/burst/user/domenico79/recommenderBibSonomy BuRST Feed for /user/domenico79/recommender2008-08-21T05:13:58+02:00Tag Boosterhttp://www.bibsonomy.org/bibtex/21af9877eebe38cdedc6578967ae76d8a/domenico79domenico792008-03-12T18:50:45+01:00collaborative recommender tagging <span style="color:#555555;">M. <a href="http://www.bibsonomy.org/author/Vojnovic">Vojnovic</a> and J. <a href="http://www.bibsonomy.org/author/Cruise">Cruise</a> and D. <a href="http://www.bibsonomy.org/author/Gunawardena">Gunawardena</a> and P. <a href="http://www.bibsonomy.org/author/Marbach">Marbach</a> </span>(<em>2007</em>)Wed Mar 12 18:50:45 CET 2008MSR-TR-2007-06Ranking and Suggesting Tags in Collaborative Tagging Applications2007collaborative recommender tagging Evaluating collaborative filtering recommender systemshttp://www.bibsonomy.org/bibtex/2bdd3980bb3c297d1b84ceb0c7729d397/domenico79domenico792008-01-22T15:18:39+01:00evaluation recommender collaborative filtering <span style="color:#555555;">Jonathan L. <a href="http://www.bibsonomy.org/author/Herlocker">Herlocker</a> and Joseph A. <a href="http://www.bibsonomy.org/author/Konstan">Konstan</a> and Loren G. <a href="http://www.bibsonomy.org/author/Terveen">Terveen</a> and John T. <a href="http://www.bibsonomy.org/author/Riedl">Riedl</a> </span><em>ACM Trans. Inf. Syst.</em><em>22(1):5--53</em>(<em>2004</em>)Tue Jan 22 15:18:39 CET 2008New York, NY, USAACM Trans. Inf. Syst.15--53Evaluating collaborative filtering recommender systems222004evaluation recommender collaborative 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.http://www.bibsonomy.org/bibtex/2fc8c03e106e0934a550b76e3b94bab6d/domenico79domenico792007-04-24T16:00:43+02:00www2006 recommender suggestions collaborative web2.0 tagging folksonomy <span style="color:#555555;">Z. <a href="http://www.bibsonomy.org/author/Xu">Xu</a> and Y. <a href="http://www.bibsonomy.org/author/Fu">Fu</a> and J. <a href="http://www.bibsonomy.org/author/Mao">Mao</a> and D. <a href="http://www.bibsonomy.org/author/Su">Su</a> </span><em>Collaborative Web Tagging Workshop at WWW2006, Edinburgh, Scotland, May</em>(<em>2006</em>)Tue Apr 24 16:00:43 CEST 2007Collaborative Web Tagging Workshop at WWW2006, Edinburgh, Scotland, May{Towards the semantic web: Collaborative tag suggestions}2006www2006 recommender suggestions collaborative web2.0 tagging folksonomy Content organization over the Internet went through several interesting phases of evolution: from structured directories to unstructured Web search engines and more recently, to tagging as a way for aggregating information, a step towards the semantic web vision. Tagging allows ranking and data organization to directly utilize inputs from end users, enabling machine processing of Web content. Since tags are created by individual users in a free form, one important problem facing tagging is to identify most appropriate tags, while eliminating noise and spam. For this purpose, we define a set of general criteria for a good tagging system. These criteria include high coverage of multiple facets to ensure good recall, least effort to reduce the cost involved in browsing, and high popularity to ensure tag quality. We propose a collaborative tag suggestion algorithm using these criteria to spot high-quality tags. The proposed algorithm employs a goodness measure for tags derived from collective user authorities to combat spam. The goodness measure is iteratively adjusted by a reward-penalty algorithm, which also incorporates other sources of tags, e.g., content-based auto-generated tags. Our experiments based on My Web 2.0 show that the algorithm is effective.