BibSonomyburst/author/HothoBibSonomy RSS feed for /author/Hotho2012-02-15T14:14:06+01:00http://www.bibsonomy.org/bibtex/2f480ef737895d48673e051f98977d77c/pseitipseiti2012-02-14T09:59:19+01:00tagging review recommender <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/Lars">Schmidt-Thie Lars</a>, and <a href="/author/Gerd">Stum Gerd</a> </span><em>AI Commun.</em> (<em>December 2008</em>)Tue Feb 14 09:59:19 CET 2012Amsterdam, The Netherlands, The NetherlandsAI Commun.December231--247Tag recommendations in social bookmarking systems212008tagging review recommender Collaborative tagging systems allow users to assign keywords - so called &ldquo;tags&rdquo; - 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.</p> <p>In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurrences. We show that both FolkRank and collaborative filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.Tag recommendations in social bookmarking systemshttp://www.bibsonomy.org/bibtex/287d6883ebd98e8810be45d7e7e4ade96/hothohotho2012-02-14T08:29:50+01:00tagging social recommender myown folksonomy collaborative bookmarking 2012 <span class="authorEditorList"><a href="/author/Balby Marinho">L. Balby Marinho</a>, <a href="/author/Hotho">A. Hotho</a>, <a href="/author/Jäschke">R. Jäschke</a>, <a href="/author/Nanopoulos">A. Nanopoulos</a>, <a href="/author/Rendle">S. Rendle</a>, <a href="/author/Schmidt-Thieme">L. Schmidt-Thieme</a>, <a href="/author/Stumme">G. Stumme</a>, and <a href="/author/Symeonidis">P. Symeonidis</a> </span><em>SpringerBriefs in Electrical and Computer Engineering </em><em>Springer, </em>(<em>February 2012</em>)Tue Feb 14 08:29:50 CET 2012febSpringerBriefs in Electrical and Computer EngineeringRecommender Systems for Social Tagging Systems2012tagging social recommender myown folksonomy collaborative bookmarking 2012 Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.http://www.bibsonomy.org/bibtex/287d6883ebd98e8810be45d7e7e4ade96/jaeschkejaeschke2012-02-13T12:52:23+01:00tagging social recommender myown folksonomy collaborative bookmarking 2012 <span class="authorEditorList"><a href="/author/Balby Marinho">L. Balby Marinho</a>, <a href="/author/Hotho">A. Hotho</a>, <a href="/author/Jäschke">R. Jäschke</a>, <a href="/author/Nanopoulos">A. Nanopoulos</a>, <a href="/author/Rendle">S. Rendle</a>, <a href="/author/Schmidt-Thieme">L. Schmidt-Thieme</a>, <a href="/author/Stumme">G. Stumme</a>, and <a href="/author/Symeonidis">P. Symeonidis</a> </span><em>SpringerBriefs in Electrical and Computer Engineering </em><em>Springer, </em>(<em>February 2012</em>)Mon Feb 13 12:52:23 CET 2012febSpringerBriefs in Electrical and Computer EngineeringRecommender Systems for Social Tagging Systems2012tagging social recommender myown folksonomy collaborative bookmarking 2012 Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.http://www.bibsonomy.org/bibtex/272e33772dff2bb6fe5cac32876e0e2da/peter.b825peter.b8252012-02-11T19:24:16+01:00Social Tagging;Semantic Web <span class="authorEditorList"><a href="/author/Cattuto">Ciro Cattuto</a>, <a href="/author/Benz">Dominik Benz</a>, <a href="/author/Hotho">Andreas Hotho</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>The Semantic Web - ISWC 2008, </em><em>volume 5318 of Lecture Notes in Computer Science, </em><em>page 615--631. </em><em>Berlin u.a., </em><em>Springer, </em>(<em>2008</em>)Sat Feb 11 19:24:16 CET 2012Berlin [u.a.]The Semantic Web - ISWC 2008615--631Lecture Notes in Computer ScienceSemantic Grounding of Tag Relatedness in Social Bookmarking Systems53182008Social Tagging;Semantic Web Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For tasks like synonym detection and discovery of concept hierarchies, many researchers introduced measures of tag similarity. Even though most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptions on the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity in terms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures of tag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding is provided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measures of semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of the investigated similarity measures and indicates which ones are better suited in the context of a given semantic application.http://www.bibsonomy.org/bibtex/21d56ef654ef3a20214665b5555bca2f3/peter.b825peter.b8252012-02-11T19:24:16+01:00imported <span class="authorEditorList"><a href="/author/Krause">Beate Krause</a>, <a href="/author/Hotho">Andreas Hotho</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Advances in Information Retrieval, </em><em>volume 4956 of Lecture Notes in Computer Science, </em><em>page 101--113. </em><em>Berlin u.a., </em><em>Springer, </em>(<em>2008</em>)Sat Feb 11 19:24:16 CET 2012Berlin [u.a.]Advances in Information Retrieval101--113Lecture Notes in Computer ScienceA Comparison of Social Bookmarking with Traditional Search49562008imported Social bookmarking systems allow users to store links to internet resources on a web page. As social bookmarking systems are growing in popularity, search algorithms have been developed that transfer the idea of link-based rankings in the Web to a social bookmarking system's data structure. These rankings differ from traditional search engine rankings in that they incorporate the rating of users. In this study, we compare search in social bookmarking systems with traditional Web search. In the first part, we compare the user activity and behaviour in both kinds of systems, as well as the overlap of the underlying sets of URLs. In the second part, we compare graph-based and vector space rankings for social bookmarking systems with commercial search engine rankings. Our experiments are performed on data of the social bookmarking system Del.icio.us and on rankings and log data from Google, MSN, and AOL. We will show that part of the difference between the systems is due to different behaviour (e.g. the concatenation of multi-word lexems to single terms in Del.icio.us), and that real-world events may trigger similar behaviour in both kinds of systems. We will also show that a graph-based ranking approach on folksonomies yields results that are closer to the rankings of the commercial search engines than vector space retrieval, and that the correlation is high in particular for the domains that are well covered by the social bookmarking system.http://www.bibsonomy.org/bibtex/2a8c40df83b1d7a3a25b0000afa66ecb2/peter.b825peter.b8252012-02-11T19:24:16+01:00imported <span class="authorEditorList"><a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Schmitz">Christoph Schmitz</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>The Semantic Web: Research and Applications, </em><em>volume 4011 of Lecture Notes in Computer Science, </em><em>page 411--426. </em><em>Berlin u.a., </em><em>Springer, </em>(<em>2006</em>)Sat Feb 11 19:24:16 CET 2012Berlin [u.a.]The Semantic Web: Research and Applications411--426Lecture Notes in Computer ScienceInformation Retrieval in Folksonomies: Search and Ranking40112006imported Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. At the moment, however, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset.http://www.bibsonomy.org/bibtex/27a2aae07b168df7f70118a65ff0d73bf/peter.b825peter.b8252012-02-11T19:24:16+01:00imported <span class="authorEditorList"><a href="/author/Illig">Jens Illig</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Jäschke">Robert Jäschke</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Knowledge processing and data analysis, </em><em>volume 6581 of Lecture notes in artificial intelligence, </em><em>page 136--149. </em><em>Berlin, </em><em>Springer, </em>(<em>2007</em>)Sat Feb 11 19:24:16 CET 2012BerlinKnowledge processing and data analysis136--149Lecture notes in artificial intelligenceA comparison of content-based tag recommendations in folksonomy systems65812007imported http://www.bibsonomy.org/bibtex/27d41d332cccc3e7ba8e7dadfb7996337/hothohotho2012-02-09T09:26:57+01:002012 bookmarking challenge collaborative dc09 discovery folksonomy myown recommender rsdc08 social tagging <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Mitzlaff">Folke Mitzlaff</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Recommender Systems for the Social Web, </em><em>volume 32 of Intelligent Systems Reference Library, </em><em>Springer, </em><em>Berlin/Heidelberg, </em>(<em>2012</em>)Thu Feb 09 09:26:57 CET 2012Berlin/HeidelbergRecommender Systems for the Social Web65--87Intelligent Systems Reference LibraryChallenges in Tag Recommendations for Collaborative Tagging Systems3220122012 bookmarking challenge collaborative dc09 discovery folksonomy myown recommender rsdc08 social tagging Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.http://www.bibsonomy.org/bibtex/27d41d332cccc3e7ba8e7dadfb7996337/stummestumme2012-02-06T14:59:32+01:002012 bookmarking challenge collaborative dc09 discovery folksonomy myown recommender rsdc08 social tagging <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Mitzlaff">Folke Mitzlaff</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Recommender Systems for the Social Web, </em><em>volume 32 of Intelligent Systems Reference Library, </em><em>Springer, </em><em>Berlin/Heidelberg, </em>(<em>2012</em>)Mon Feb 06 14:59:32 CET 2012Berlin/HeidelbergRecommender Systems for the Social Web65--87Intelligent Systems Reference LibraryChallenges in Tag Recommendations for Collaborative Tagging Systems3220122012 bookmarking challenge collaborative dc09 discovery folksonomy myown recommender rsdc08 social tagging Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.http://www.bibsonomy.org/bibtex/27d41d332cccc3e7ba8e7dadfb7996337/dbenzdbenz2012-02-06T14:04:20+01:002012 challenge collaborative recommendation tagging <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Mitzlaff">Folke Mitzlaff</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Recommender Systems for the Social Web, </em><em>volume 32 of Intelligent Systems Reference Library, </em><em>Springer, </em><em>Berlin/Heidelberg, </em>(<em>2012</em>)Mon Feb 06 14:04:20 CET 2012Berlin/HeidelbergRecommender Systems for the Social Web65--87Intelligent Systems Reference LibraryChallenges in Tag Recommendations for Collaborative Tagging Systems3220122012 challenge collaborative recommendation tagging Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.http://www.bibsonomy.org/bibtex/2b645572f4c635b2d674126f26d4303d6/sdosdo2012-02-06T14:02:41+01:00challenge collaborative recommendation system tagging <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Mitzlaff">Folke Mitzlaff</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Recommender Systems for the Social Web, </em><em>volume 32 of Intelligent Systems Reference Library, </em><em>Springer, </em><em>Berlin/Heidelberg, </em>(<em>2012</em>)Mon Feb 06 14:02:41 CET 2012Berlin/HeidelbergRecommender Systems for the Social Web65--87Intelligent Systems Reference LibraryChallenges in Tag Recommendations for Collaborative Tagging Systems322012challenge collaborative recommendation system tagging Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.http://www.bibsonomy.org/bibtex/27d41d332cccc3e7ba8e7dadfb7996337/jaeschkejaeschke2012-02-06T13:47:57+01:002012 bookmarking challenge collaborative dc09 discovery folksonomy myown recommender rsdc08 social tagging <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Mitzlaff">Folke Mitzlaff</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Recommender Systems for the Social Web, </em><em>volume 32 of Intelligent Systems Reference Library, </em><em>Springer, </em><em>Berlin/Heidelberg, </em>(<em>2012</em>)Mon Feb 06 13:47:57 CET 2012Berlin/HeidelbergRecommender Systems for the Social Web65--87Intelligent Systems Reference LibraryChallenges in Tag Recommendations for Collaborative Tagging Systems3220122012 bookmarking challenge collaborative dc09 discovery folksonomy myown recommender rsdc08 social tagging Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.http://www.bibsonomy.org/bibtex/2c791a9a4a94d714e3582dabc907e0edd/dblpdblp2012-02-06T00:00:00+01:00dblp <span class="authorEditorList"><a href="/author/Cattuto">Ciro Cattuto</a>, and <a href="/author/Hotho">Andreas Hotho</a> </span><em>The New Review of Hypermedia and Multimedia</em> <em>17(3):241-242</em> (<em>2011</em>)Mon Feb 06 00:00:00 CET 2012The New Review of Hypermedia and Multimedia3241-242Introduction to the Special Issue on Social Linking and Hypermedia.172011dblp http://www.bibsonomy.org/bibtex/2df97de393d3421ef2e20384ddde16ab1/dblpdblp2012-02-02T00:00:00+01:00dblp <span class="authorEditorList"><a href="/author/Krause">Beate Krause</a>, <a href="/author/Lerch">Hana Lerch</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Roßnagel">Alexander Roßnagel</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Informatik Spektrum</em> <em>35(1):12-23</em> (<em>2012</em>)Thu Feb 02 00:00:00 CET 2012Informatik Spektrum112-23Datenschutz im Web 2.0 am Beispiel des sozialen Tagging-Systems BibSonomy.352012dblp http://www.bibsonomy.org/bibtex/20f45e870093c053e6f41f54c14bda46b/folkefolke2012-01-30T09:27:21+01:002011 assessment community myown <span class="authorEditorList"><a href="/author/Mitzlaff">Folke Mitzlaff</a>, <a href="/author/Atzmueller">Martin Atzmueller</a>, <a href="/author/Benz">Dominik Benz</a>, <a href="/author/Hotho">Andreas Hotho</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Analysis of Social Media and Ubiquitous Data, </em><em>volume 6904 of LNAI, </em>(<em>2011</em>)Mon Jan 30 09:27:21 CET 2012Analysis of Social Media and Ubiquitous DataLNAI{Community Assessment using Evidence Networks}690420112011 assessment community myown http://www.bibsonomy.org/bibtex/200a8f31185a34957eb16d500d7d51398/hothohotho2012-01-20T09:29:21+01:002011 datamining detection myown privacy spam web20 <span class="authorEditorList"><a href="/author/Bullock">Beate Navarro Bullock</a>, <a href="/author/Lerch">Hana Lerch</a>, <a href="/author/Roßnagel">Alexander Roßnagel</a>, <a href="/author/Hotho">Andreas Hotho</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies, </em><em>page 15:1--15:8. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2011</em>)Fri Jan 20 09:29:21 CET 2012New York, NY, USAProceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies15:1--15:8i-KNOW '11Privacy-aware spam detection in social bookmarking systems20112011 datamining detection myown privacy spam web20 With the increased popularity of Web 2.0 services in the last years data privacy has become a major concern for users. The more personal data users reveal, the more difficult it becomes to control its disclosure in the web. However, for Web 2.0 service providers, the data provided by users is a valuable source for offering effective, personalised data mining services. One major application is the detection of spam in social bookmarking systems: in order to prevent a decrease of content quality, providers need to distinguish spammers and exclude them from the system. They thereby experience a conflict of interests: on the one hand, they need to identify spammers based on the information they collect about users, on the other hand, they need to respect privacy concerns and process as few personal data as possible. It would therefore be of tremendous help for system developers and users to know which personal data are needed for spam detection and which can be ignored. In this paper we address these questions by presenting a data privacy aware feature engineering approach. It consists of the design of features for spam classification which are evaluated according to both, performance and privacy conditions. Experiments using data from the social bookmarking system BibSonomy show that both conditions must not exclude each other.Privacy-aware spam detection in social bookmarking systemshttp://www.bibsonomy.org/bibtex/2be3c753af98ab591b4f31d349513b461/enitsirhcenitsirhc2012-01-19T16:05:26+01:002008 mining data myown ubiquitous kdd kdubiq <span class="authorEditorList"><a href="/author/May">Michael May</a>, <a href="/author/Berendt">Bettina Berendt</a>, <a href="/author/Cornuéjols">Antoine Cornuéjols</a>, <a href="/author/Gama">Jõao Gama</a>, <a href="/author/Giannotti">Fosca Giannotti</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Malerba">Donato Malerba</a>, <a href="/author/Menesalvas">Ernestina Menesalvas</a>, <a href="/author/Morik">Katharina Morik</a>, <a href="/author/Pedersen">Rasmus Pedersen</a>, <a href="/author/Saitta">Lorenza Saitta</a>, <a href="/author/Saygin">Yücel Saygin</a>, <a href="/author/Schuster">Assaf Schuster</a>, and <a href="/author/Vanhoof">Koen Vanhoof</a> </span><em>Next Generation of Data Mining Chapman &amp; Hall/Crc Data Mining and Knowledge Discovery Series, </em><em>Chapman &amp; Hall/CRC, </em><em>1 edition, </em>(<em>2008</em>)Thu Jan 19 16:05:26 CET 2012Next Generation of Data Mining (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)1Research Challenges in Ubiquitous Knowledge Discovery20082008 mining data myown ubiquitous kdd kdubiq Amazon.com: Next Generation of Data Mining (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series): Hillol Kargupta, Jiawei Han, Philip S. Yu, Rajeev Motwani, Vipin Kumar: Bookshttp://www.bibsonomy.org/bibtex/2b96a6cf5d9999ca9063b7d7cd229e50d/enitsirhcenitsirhc2012-01-19T15:57:22+01:00rfid social 2011 myown computing conference ubiquitous network conferator <span class="authorEditorList"><a href="/author/Atzmueller">Martin Atzmueller</a>, <a href="/author/Benz">Dominik Benz</a>, <a href="/author/Doerfel">Stephan Doerfel</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Macek">Bjoern Elmar Macek</a>, <a href="/author/Mitzlaff">Folke Mitzlaff</a>, <a href="/author/Scholz">Christoph Scholz</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Information Technology</em> <em>53(3):101--107</em> (<em>May 2011</em>)Thu Jan 19 15:57:22 CET 2012MünchenInformation Technologymay3101--107Enhancing Social Interactions at Conferences532011rfid social 2011 myown computing conference ubiquitous network conferator Conferator is a novel social conference system that provides the management of social interactions and context information in ubiquitous and social environments. Using RFID and social networking technology, Conferator provides the means for effective management of personal contacts and according conference information before, during and after a conference. We describe the system in detail, before we analyze and discuss results of a typical application of the Conferator system.http://www.bibsonomy.org/bibtex/2c9437d5ec56ba949f533aeec00f571e3/nosebrainnosebrain2012-01-19T12:34:45+01:00bibsonomy bookmark publication sharing social system <span class="authorEditorList"><a href="/author/Benz">Dominik Benz</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Krause">Beate Krause</a>, <a href="/author/Mitzlaff">Folke Mitzlaff</a>, <a href="/author/Schmitz">Christoph Schmitz</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>The VLDB Journal</em> <em>19(6):849--875</em> (<em>December 2010</em>)Thu Jan 19 12:34:45 CET 2012Berlin / HeidelbergThe VLDB Journaldec6849--875The Social Bookmark and Publication Management System BibSonomy192010bibsonomy bookmark publication sharing social system Social resource sharing systems are central elements of the Web 2.0 and use the same kind of lightweight knowledge representation, called folksonomy. Their large user communities and ever-growing networks of user-generated content have made them an attractive object of investigation for researchers from different disciplines like Social Network Analysis, Data Mining, Information Retrieval or Knowledge Discovery. In this paper, we summarize and extend our work on different aspects of this branch of Web 2.0 research, demonstrated and evaluated within our own social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind. We structure this presentation along the different interaction phases of a user with our system, coupling the relevant research questions of each phase with the corresponding implementation issues. This approach reveals in a systematic fashion important aspects and results of the broad bandwidth of folksonomy research like capturing of emergent semantics, spam detection, ranking algorithms, analogies to search engine log data, personalized tag recommendations and information extraction techniques. We conclude that when integrating a real-life application like BibSonomy into research, certain constraints have to be considered; but in general, the tight interplay between our scientific work and the running system has made BibSonomy a valuable platform for demonstrating and evaluating Web 2.0 research.http://www.bibsonomy.org/bibtex/2deec7e33ba811cd4dfacb29e6dc0fb9c/hothohotho2012-01-19T10:29:03+01:002011 localization myown rfid <span class="authorEditorList"><a href="/author/Scholz">Christoph Scholz</a>, <a href="/author/Doerfel">Stephan Doerfel</a>, <a href="/author/Atzmueller">Martin Atzmueller</a>, <a href="/author/Hotho">Andreas Hotho</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>ECML/PKDD 3, </em><em>page 129-144. </em>(<em>2011</em>)<em>misc = 27 . </em>Thu Jan 19 10:29:03 CET 2012ECML/PKDD (3)misc = 27129-144Resource-Aware On-Line RFID Localization Using Proximity Data20112011 localization myown rfid accepted