http://www.bibsonomy.org/burst/user/jaeschke/tagBibSonomy BuRST Feed for /user/jaeschke/tag2008-10-16T09:20:54+02:00http://www.bibsonomy.org/bibtex/2dd5ccfe1f5bef87cefd2a000e31d6558/jaeschkejaeschke2008-10-15T15:38:57+02:00collaborative tagging tag recommender autotag blog <span style="color:#555555;">Gilad <a href="http://www.bibsonomy.org/author/Mishne">Mishne</a> </span><em>WWW '06: Proceedings of the 15th international conference on World Wide Web, </em><em>Seite953--954. </em><em>New York, NY, USA, </em><em>ACM Press, </em>(<em>2006</em>) <em>paper presented at the poster track . </em>Wed Oct 15 15:38:57 CEST 2008New York, NY, USAWWW '06: Proceedings of the 15th international conference on World Wide Webpaper presented at the poster track953--954AutoTag: a collaborative approach to automated tag assignment for weblog posts2006collaborative tagging tag recommender autotag blog This paper describes AutoTag, a tool which suggests tags for weblog posts using collaborative filtering methods. An evaluation of AutoTag on a large collection of posts shows good accuracy; coupled with the blogger's final quality control, AutoTag assists both in simplifying the tagging process and in improving its quality.Tag Boosterhttp://www.bibsonomy.org/bibtex/21af9877eebe38cdedc6578967ae76d8a/jaeschkejaeschke2008-10-15T15:05:34+02:00collaborative tagging tag recommender <span style="color:#555555;">M. <a href="http://www.bibsonomy.org/author/Vojnovic">Vojnovic</a> und J. <a href="http://www.bibsonomy.org/author/Cruise">Cruise</a> und D. <a href="http://www.bibsonomy.org/author/Gunawardena">Gunawardena</a> und P. <a href="http://www.bibsonomy.org/author/Marbach">Marbach</a> </span>(<em>2007</em>)Wed Oct 15 15:05:34 CEST 2008MSR-TR-2007-06Ranking and Suggesting Tags in Collaborative Tagging Applications2007collaborative tagging tag recommender We consider collaborative tagging systems where users can attach tags to information objects. Such systems are widely used to add keywords meta-data to photos, videos, or web pages (social bookmarking applications). The meta-data is then used by information retrieval mechanism to provide accurate query answers. To that end, the goal of collaborative tagging systems is to quickly discover the true ranking of a tag for an information object with respect to a given ranking criteria. In this paper, we consider the popularity rank as a ranking criteria. Many collaborative tagging systems help users tagging of an object by making suggestions based on the tagging history of an information object. The problem with making tag suggestions is that they may reinforce some tags and the system fails to discover the true popularity rank of a tag for a given information object. To investigate this issue, we propose and study several algorithms for ranking and suggesting tags in collaborative tagging systems where we focus on the following design objectives: (a) learn true popularity rank, (b) make relevant suggestions, and (c) learn fast. We find that simple incremental updates of the suggestion set, which besides suggestion set size require no configuration constants, offer good performance. Performance is evaluated by analysis and numerical results for which we used a dataset of complete tagging histories of urls that we crawled from a popular social bookmarking web service over a month period.http://www.bibsonomy.org/bibtex/2fc8c03e106e0934a550b76e3b94bab6d/jaeschkejaeschke2008-10-15T15:01:15+02:00tagging tag recommender <span style="color:#555555;">Z. <a href="http://www.bibsonomy.org/author/Xu">Xu</a> und Y. <a href="http://www.bibsonomy.org/author/Fu">Fu</a> und J. <a href="http://www.bibsonomy.org/author/Mao">Mao</a> und 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>)Wed Oct 15 15:01:15 CEST 2008Collaborative Web Tagging Workshop at WWW2006, Edinburgh, Scotland, May{Towards the semantic web: Collaborative tag suggestions}2006tagging tag recommender 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.http://www.bibsonomy.org/bibtex/2157846898c1c2a65c265a913ebac115a/jaeschkejaeschke2008-10-15T14:57:24+02:00content tagging tag similarity recommender <span style="color:#555555;">Andrew <a href="http://www.bibsonomy.org/author/Byde">Byde</a> und Hui <a href="http://www.bibsonomy.org/author/Wan">Wan</a> und Steve <a href="http://www.bibsonomy.org/author/Cayzer">Cayzer</a> </span><em>Proceedings of the International Conference on Weblogs and Social Media, </em><em>March2007. </em>Wed Oct 15 14:57:24 CEST 2008Proceedings of the International Conference on Weblogs and Social MediaMarchPersonalized Tag Recommendations via Tagging and Content-based Similarity Metrics2007content tagging tag similarity recommender This short paper describes a novel technique for generating personalized tag recommendations for users of social book- marking sites such as del.icio.us. Existing techniques recom- mend tags on the basis of their popularity among the group of all users; on the basis of recent use; or on the basis of simple heuristics to extract keywords from the url being tagged. Our method is designed to complement these approaches, and is based on recommending tags from urls that are similar to the one in question, according to two distinct similarity metrics, whose principal utility covers complementary cases.http://www.bibsonomy.org/bibtex/258f1705eb270c20c6aaea2377f88e664/jaeschkejaeschke2008-10-15T14:50:02+02:00annotation personalized p-tag tagging tag recommender web <span style="color:#555555;">Paul Alexandru <a href="http://www.bibsonomy.org/author/Chirita">Chirita</a> und Stefania <a href="http://www.bibsonomy.org/author/Costache">Costache</a> und Wolfgang <a href="http://www.bibsonomy.org/author/Nejdl">Nejdl</a> und Siegfried <a href="http://www.bibsonomy.org/author/Handschuh">Handschuh</a> </span><em>WWW '07: Proceedings of the 16th international conference on World Wide Web, </em><em>Seite845--854. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2007</em>)Wed Oct 15 14:50:02 CEST 2008New York, NY, USAWWW '07: Proceedings of the 16th international conference on World Wide Web845--854P-TAG: large scale automatic generation of personalized annotation tags for the web2007annotation personalized p-tag tagging tag recommender web The success of the Semantic Web depends on the availability of Web pages annotated with metadata. Free form metadata or tags, as used in social bookmarking and folksonomies, have become more and more popular and successful. Such tags are relevant keywords associated with or assigned to a piece of information (e.g., a Web page), describing the item and enabling keyword-based classification. In this paper we propose P-TAG, a method which automatically generates personalized tags for Web pages. Upon browsing a Web page, P-TAG produces keywords relevant both to its textual content, but also to the data residing on the surfer's Desktop, thus expressing a personalized viewpoint. Empirical evaluations with several algorithms pursuing this approach showed very promising results. We are therefore very confident that such a user oriented automatic tagging approach can provide large scale personalized metadata annotations as an important step towards realizing the Semantic Web.http://www.bibsonomy.org/bibtex/2bb8e778a61c315ad946dfec71b13cc8b/jaeschkejaeschke2008-10-15T14:47:26+02:00tagging tag recommender tagassist suggestion blog <span style="color:#555555;">Sanjay <a href="http://www.bibsonomy.org/author/Sood">Sood</a> und Sara <a href="http://www.bibsonomy.org/author/Owsley">Owsley</a> und Kristian <a href="http://www.bibsonomy.org/author/Hammond">Hammond</a> und Larry <a href="http://www.bibsonomy.org/author/Birnbaum">Birnbaum</a> </span><em>Proceedings of the International Conference on Weblogs and Social Media (ICWSM 2007), </em>(<em>2007</em>)Wed Oct 15 14:47:26 CEST 2008Proceedings of the International Conference on Weblogs and Social Media (ICWSM 2007){TagAssist: Automatic Tag Suggestion for Blog Posts}2007tagging tag recommender tagassist suggestion blog In this paper, we describe a system called TagAssist that provides tag suggestions for new blog posts by utilizing existing tagged posts. The system is able to increase the quality of suggested tags by performing lossless compression over existing tag data. In addition, the system employs a set of metrics to evaluate the quality of a potential tag suggestion. Coupled with the ability for users to manually add tags, TagAssist can ease the burden of tagging and increase the utility of retrieval and browsing systems built on top of tagging data.Personalized tag suggestion for flickrhttp://www.bibsonomy.org/bibtex/2070715bf0c1f010785dbc7af22afe838/jaeschkejaeschke2008-10-15T14:42:15+02:00personalized flickr tagging tag recommender suggestion <span style="color:#555555;">Nikhil <a href="http://www.bibsonomy.org/author/Garg">Garg</a> und Ingmar <a href="http://www.bibsonomy.org/author/Weber">Weber</a> </span><em>WWW '08: Proceeding of the 17th international conference on World Wide Web, </em><em>Seite1063--1064. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2008</em>)Wed Oct 15 14:42:15 CEST 2008New York, NY, USAWWW '08: Proceeding of the 17th international conference on World Wide Web1063--1064Personalized tag suggestion for flickr2008personalized flickr tagging tag recommender suggestion We present a system for personalized tag suggestion for Flickr: While the user is entering/selecting new tags for a particular picture, the system is suggesting related tags to her, based on the tags that she or other people have used in the past along with (some of) the tags already entered. The suggested tags are dynamically updated with every additional tag entered/selected. We describe three algorithms which can be applied to this problem. In experiments, our best-performing method yields an improvement in precision of 10-15% over a baseline method very similar to the system currently used by Flickr. Our system is accessible at http://ltaa5.epfl.ch/flickr-tags/. To the best of our knowledge, this is the first study on tag suggestion in a setting where (i) no full text information is available, such as for blogs, (ii) no item has been tagged by more than one person, such as for social bookmarking sites, and (iii) suggestions are dynamically updated, requiring efficient yet effective algorithms.Social tag predictionhttp://www.bibsonomy.org/bibtex/2f089aabc540f9bfaffffb0eddfbb5260/jaeschkejaeschke2008-10-15T14:39:37+02:00tagging tag social recommender prediction <span style="color:#555555;">Paul <a href="http://www.bibsonomy.org/author/Heymann">Heymann</a> und Daniel <a href="http://www.bibsonomy.org/author/Ramage">Ramage</a> und Hector <a href="http://www.bibsonomy.org/author/Garcia-Molina">Garcia-Molina</a> </span><em>SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, </em><em>Seite531--538. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2008</em>)Wed Oct 15 14:39:37 CEST 2008New York, NY, USASIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval531--538Social tag prediction2008tagging tag social recommender prediction In this paper, we look at the "social tag prediction" problem. Given a set of objects, and a set of tags applied to those objects by users, can we predict whether a given tag could/should be applied to a particular object? We investigated this question using one of the largest crawls of the social bookmarking system del.icio.us gathered to date. For URLs in del.icio.us, we predicted tags based on page text, anchor text, surrounding hosts, and other tags applied to the URL. We found an entropy-based metric which captures the generality of a particular tag and informs an analysis of how well that tag can be predicted. We also found that tag-based association rules can produce very high-precision predictions as well as giving deeper understanding into the relationships between tags. Our results have implications for both the study of tagging systems as potential information retrieval tools, and for the design of such systems.Flickr tag recommendation based on collective knowledgehttp://www.bibsonomy.org/bibtex/2a162454457c5ddd6fa4d82cca3aa6b8c/jaeschkejaeschke2008-10-15T13:53:03+02:00flickr bookmarking tagging tag social recommender <span style="color:#555555;">B&#246;rkur <a href="http://www.bibsonomy.org/author/Sigurbjörnsson">Sigurbj&#246;rnsson</a> und Roelof van <a href="http://www.bibsonomy.org/author/Zwol">Zwol</a> </span><em>WWW '08: Proceeding of the 17th international conference on World Wide Web, </em><em>Seite327--336. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2008</em>)Wed Oct 15 13:53:03 CEST 2008New York, NY, USAWWW '08: Proceeding of the 17th international conference on World Wide Web327--336Flickr tag recommendation based on collective knowledge2008flickr bookmarking tagging tag social recommender http://www.bibsonomy.org/bibtex/299ffb0c3a76afe508f5ff6b219f72515/jaeschkejaeschke2007-05-18T11:34:48+02:00visual tagging tag cloud folksonomy interface <span style="color:#555555;">Y. <a href="http://www.bibsonomy.org/author/Hassan-Montero">Hassan-Montero</a> und V. <a href="http://www.bibsonomy.org/author/Herrero-Solana">Herrero-Solana</a> </span><em>Proc. InSciT 2006, </em><em>Merida, Spain, </em><em>Oct2006. </em>Fri May 18 11:34:48 CEST 2007Merida, SpainProc. InSciT 2006Oct{Improving Tag-Clouds as Visual Information Retrieval Interfaces}2006visual tagging tag cloud folksonomy interface 25--28http://www.bibsonomy.org/bibtex/2a62decf2da83f2d9e10ff7846296699b/jaeschkejaeschke2006-04-21T11:58:02+02:00tagging tag web2.0 folksonomy <span style="color:#555555;">Marieke <a href="http://www.bibsonomy.org/author/Guy">Guy</a> und Emma <a href="http://www.bibsonomy.org/author/Tonkin">Tonkin</a> </span><em>D-Lib Magazine</em><em>January2006. </em><em>ISSN 1082-9873 . </em>Fri Apr 21 11:58:02 CEST 2006D-Lib MagazineJanuaryISSN 1082-98731Folksonomies - Tidying up Tags?122006tagging tag web2.0 folksonomy http://www.bibsonomy.org/bibtex/2dc72abb1df242c52bf2c4fa19790dcec/jaeschkejaeschke2006-04-20T11:34:33+02:00time tagging seminar2006 visualizing tag folksonomy <span style="color:#555555;">M. <a href="http://www.bibsonomy.org/author/Dubinko">Dubinko</a> und R. <a href="http://www.bibsonomy.org/author/Kumar">Kumar</a> und J. <a href="http://www.bibsonomy.org/author/Magnani">Magnani</a> und J. <a href="http://www.bibsonomy.org/author/Novak">Novak</a> und P. <a href="http://www.bibsonomy.org/author/Raghavan">Raghavan</a> und A. <a href="http://www.bibsonomy.org/author/Tomkins">Tomkins</a> </span><em>Proceedings of the 15th International WWW Conference, </em><em>May2006. </em>Thu Apr 20 11:34:33 CEST 2006Proceedings of the 15th International WWW ConferenceMayVisualizing Tags over Time2006time tagging seminar2006 visualizing tag folksonomy 23-25