BibSonomyburst/group/kde/myownBibSonomy RSS feed for /group/kde/myown2012-02-15T21:50:23+01:00http://www.bibsonomy.org/bibtex/287d6883ebd98e8810be45d7e7e4ade96/hothohotho2012-02-14T08:29:50+01:002012 bookmarking collaborative folksonomy myown recommender social tagging <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 Systems20122012 bookmarking collaborative folksonomy myown recommender social tagging 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+01:002012 bookmarking collaborative folksonomy myown recommender social tagging <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 Systems20122012 bookmarking collaborative folksonomy myown recommender social tagging 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/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/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/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/24cf42ebabd9a670c70bee456affda285/hothohotho2012-01-27T14:02:47+01:002011 analysis data media myown social ubiquitous <span class="authorEditorList"><a href="/author/Atzmueller">Martin Atzmueller</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Strohmaier">Markus Strohmaier</a>, and <a href="/author/Chin">Alvin Chin</a> (Eds.). </span><em>volume 6904 of Lecture Notes in Computer Science, </em><em>Springer, </em>(<em>2011</em>)Fri Jan 27 14:02:47 CET 2012MSM/MUSELecture Notes in Computer ScienceAnalysis of Social Media and Ubiquitous Data - International Workshops MSM 2010, Toronto, Canada, June 13, 2010, and MUSE 2010, Barcelona, Spain, September 20, 2010, Revised Selected Papers690420112011 analysis data media myown social ubiquitous http://www.bibsonomy.org/bibtex/24cf42ebabd9a670c70bee456affda285/atzmuelleratzmueller2012-01-27T10:51:13+01:002011 analysis data itegpub media myown social ubiquitous <span class="authorEditorList"><a href="/author/Atzmueller">Martin Atzmueller</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Strohmaier">Markus Strohmaier</a>, and <a href="/author/Chin">Alvin Chin</a> (Eds.). </span><em>volume 6904 of Lecture Notes in Computer Science, </em><em>Springer, </em>(<em>2011</em>)Fri Jan 27 10:51:13 CET 2012MSM/MUSELecture Notes in Computer ScienceAnalysis of Social Media and Ubiquitous Data - International Workshops MSM 2010, Toronto, Canada, June 13, 2010, and MUSE 2010, Barcelona, Spain, September 20, 2010, Revised Selected Papers690420112011 analysis data itegpub media myown social ubiquitous http://www.bibsonomy.org/bibtex/2cb16be899e3a5f45892ed91a405b2b76/atzmuelleratzmueller2012-01-26T10:54:10+01:002011 data discovery itegpub mining myown <span class="authorEditorList"><a href="/author/Krohn">Michael Krohn</a>, <a href="/author/Klein">Niklas Klein</a>, <a href="/author/Jandt">Silke Jandt</a>, <a href="/author/Atzmueller">Martin Atzmueller</a>, <a href="/author/Scholz">Christoph Scholz</a>, <a href="/author/Hoffmann">Holger Hoffmann</a>, <a href="/author/Kniewel">Romy Kniewel</a>, and <a href="/author/Evers">Christoph Evers</a> </span><em>Praxis der Informationsverarbeitung und Kommunikation</em> <em>34(4):238-240</em> (<em>2011</em>)Thu Jan 26 10:54:10 CET 2012Praxis der Informationsverarbeitung und Kommunikation4238-240Das Forschungszentrum für Informationstechnik-Gestaltung (ITeG)3420112011 data discovery itegpub mining myown http://www.bibsonomy.org/bibtex/2cb16be899e3a5f45892ed91a405b2b76/cscholzcscholz2012-01-24T11:02:25+01:002011 itegpub myown <span class="authorEditorList"><a href="/author/Krohn">Michael Krohn</a>, <a href="/author/Klein">Niklas Klein</a>, <a href="/author/Jandt">Silke Jandt</a>, <a href="/author/Atzmueller">Martin Atzmueller</a>, <a href="/author/Scholz">Christoph Scholz</a>, <a href="/author/Hoffmann">Holger Hoffmann</a>, <a href="/author/Kniewel">Romy Kniewel</a>, and <a href="/author/Evers">Christoph Evers</a> </span><em>Praxis der Informationsverarbeitung und Kommunikation</em> <em>34(4):238-240</em> (<em>2011</em>)Tue Jan 24 11:02:25 CET 2012Praxis der Informationsverarbeitung und Kommunikation4238-240Das Forschungszentrum für Informationstechnik-Gestaltung (ITeG)3420112011 itegpub 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/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 acceptedhttp://www.bibsonomy.org/bibtex/2b245d492f1f9fa41b62b79b6dec77241/stummestumme2012-01-17T16:41:44+01:002011 abstractness bind itegpub measuring myown social term <span class="authorEditorList"><a href="/author/Benz">Dominik Benz</a>, <a href="/author/Körner">Christian Körner</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Stumme">Gerd Stumme</a>, and <a href="/author/Strohmaier">Markus Strohmaier</a> </span><em>Proceedings of the 8th Extended Semantic Web Conference ESWC 2011, </em><em>Heraklion, Crete, </em>(<em>May 2011</em>)Tue Jan 17 16:41:44 CET 2012Heraklion, CreteProceedings of the 8th Extended Semantic Web Conference (ESWC 2011)mayOne Tag to Bind Them All: Measuring Term Abstractness in Social Metadata20112011 abstractness bind itegpub measuring myown social term Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering light-weight ontologies. For example, existing measures of tag similarity and tag relatedness have proven crucial step stones for making latent semantic relations in tagging systems explicit. However, little progress has been made on other issues, such as understanding the different levels of tag generality (or tag abstractness), which is essential for, among others, identifying hierarchical relationships between concepts. In this paper we aim to address this gap. Starting from a review of linguistic definitions of word abstractness, we first use several large-scale ontologies and taxonomies as grounded measures of word generality, including Yago, Wordnet, DMOZ and Wikitaxonomy. Then, we introduce and apply several folksonomy-based methods to measure the level of generality of given tags. We evaluate these methods by comparing them with the grounded measures. Our results suggest that the generality of tags in social tagging systems can be approximated with simple measures. Our work has implications for a number of problems related to social tagging systems, including search, tag recommendation, and the acquisition of light-weight ontologies from tagging data.http://www.bibsonomy.org/bibtex/20f45e870093c053e6f41f54c14bda46b/stummestumme2012-01-04T11:35:08+01:002011 community itegpub knowledge mining 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>)Wed Jan 04 11:35:08 CET 2012Analysis of Social Media and Ubiquitous DataLNAI{Community Assessment using Evidence Networks}690420112011 community itegpub knowledge mining myown http://www.bibsonomy.org/bibtex/2923d369285422c758398cbe92e3532cd/stummestumme2012-01-04T11:34:35+01:002011 abstractness itegpub measuring metadata myown social <span class="authorEditorList"><a href="/author/Benz">Dominik Benz</a>, <a href="/author/Körner">Christian Körner</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Stumme">Gerd Stumme</a>, and <a href="/author/Strohmaier">Markus Strohmaier</a> </span><em>Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation, </em>(<em>2011</em>)Wed Jan 04 11:34:35 CET 2012Working Notes of the LWA 2011 - Learning, Knowledge, AdaptationOne Tag to Bind Them All: Measuring Term Abstractness in Social Metadata20112011 abstractness itegpub measuring metadata myown social http://www.bibsonomy.org/bibtex/200a8f31185a34957eb16d500d7d51398/stummestumme2012-01-04T11:32:55+01:002011 aware classification data-mining detection itegpub myown social spam spam-detection web2.0 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>)Wed Jan 04 11:32:55 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 aware classification data-mining detection itegpub myown social spam spam-detection web2.0 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/225c89c0f9e4149da87e5212dd12481f0/stummestumme2012-01-04T11:32:27+01:002011 community gi info20 itegpub myown privat <span class="authorEditorList"><a href="/author/Kartal">Aliye Kartal</a>, <a href="/author/Doerfel">Stephan Doerfel</a>, <a href="/author/Roßnagel">Alexander Roßnagel</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Informatik 2011 - Informatik schafft Communities - Proceedings der 41. GI-Jahrestagung, </em><em>volume 192 of Lecture Notes in Informatics, </em><em>page 412. </em><em>Gesellschaft für Informatik e.V. GI, </em><em>Bonner Köllen Verlag, </em>(<em>October 2011</em>)Wed Jan 04 11:32:27 CET 2012Informatik 2011 - Informatik schafft Communities - Proceedings der 41. GI-Jahrestagung 10412Lecture Notes in InformaticsPrivatsphären- und Datenschutz in Community-Plattformen: Gestaltung von Online-Bewertungsportalen19220112011 community gi info20 itegpub myown privat Aufgrund der mittlerweile unüberschaubaren Vielfalt von Anwendungsmöglichkeiten des Web 2.0, findet man fast zu jedem Lebensbereich eine passende Community im Netz. Dabei steigt auch die Anzahl der Bewertungsportale stetig und betrifft längst nicht mehr nur die Bewertung von Waren, sondern erstreckt sich unterdessen auch auf Beurteilungen von Leistungen und Eigenschaften von zu bestimmten Berufsgruppen gehörenden Personen. Diese Entwicklung birgt die Gefahr, dass die dadurch gewonnenen persönlichen Daten durchaus geeignet sind, wahrheitswidrig ein übermäßig positives oder übermäßig negatives Persönlichkeitsbild des Betroffenen zu konstruieren und dadurch sein Ansehen zu beeinflussen. Im Hinblick auf Fragen im Zusammenhang mit dem Persönlichkeits- und Datenschutz soll der folgende Beitrag Maßstäbe an eine verfassungs- und datenschutzkonforme technische Gestaltung von Online-Bewertungsportalen aufzeigen.The print version of the proceedings only contains abstracts. The full paper was published on CD and is available here http://www.user.tu-berlin.de/komm/CD/paper/090222.pdfhttp://www.bibsonomy.org/bibtex/21ceba87cd6bc52faac36247a0c9f52a8/stummestumme2012-01-04T11:31:59+01:002011 COMMUNE conferator itegpub myown rfid sociopatterns <span class="authorEditorList"><a href="/author/Atzmüller">Martin Atzmüller</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>it - Information Technology</em> <em>53(3):101-107</em> (<em>2011</em>)Wed Jan 04 11:31:59 CET 2012it - Information Technology3101-107Enhancing Social Interactions at Conferences.5320112011 COMMUNE conferator itegpub myown rfid sociopatterns http://www.bibsonomy.org/bibtex/2708be7b5c269bd3a9d3d2334f858d52d/stummestumme2012-01-04T11:31:34+01:002011 collaborative itegpub myown recommender social tagging <span class="authorEditorList"><a href="/author/Balby Marinho">Leandro Balby Marinho</a>, <a href="/author/Nanopoulos">Alexandros Nanopoulos</a>, <a href="/author/Schmidt-Thieme">Lars Schmidt-Thieme</a>, <a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Stumme">Gerd Stumme</a>, and <a href="/author/Symeonidis">Panagiotis Symeonidis</a> </span><em>Recommender Systems Handbook, </em><em>Springer, </em><em>New York, </em>(<em>2011</em>)Wed Jan 04 11:31:34 CET 2012New YorkRecommender Systems Handbook615--644Social Tagging Recommender Systems20112011 collaborative itegpub myown recommender social tagging The new generation of Web applications known as (STS) is successfully established and poised for continued growth. STS are open and inherently social; features that have been proven to encourage participation. But while STS bring new opportunities, they revive old problems, such as information overload. 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 STS however, we face new challenges. Users are interested in finding not only content, but also tags and even other users. Moreover, while traditional recommender systems usually operate over 2-way data arrays, STS data is represented as a third-order tensor or a hypergraph with hyperedges denoting (user, resource, tag) triples. In this chapter, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve STS.We describe (a) novel facets of recommenders for STS, such as user, resource, and tag recommenders, (b) new approaches and algorithms for dealing with the ternary nature of STS data, and (c) recommender systems deployed in real world STS. Moreover, a concise comparison between existing works is presented, through which we identify and point out new research directions.http://www.bibsonomy.org/bibtex/2addbaaba7aec8360e23284c849e216ad/stummestumme2012-01-04T11:31:03+01:002011 conferator itegpub mygroup myown networking rfid social venus <span class="authorEditorList"><a href="/author/Behrenbruch">Kay Behrenbruch</a>, <a href="/author/Atzmueller">Martin Atzmueller</a>, <a href="/author/Kniewel">Romy Kniewel</a>, <a href="/author/Hoberg">Sebastian Hoberg</a>, <a href="/author/Stumme">Gerd Stumme</a>, and <a href="/author/Schmidt">Ludger Schmidt</a> </span><em>GfA-Frühjahrskongress, </em><em>Chemnitz, </em>(<em>2011</em>)Wed Jan 04 11:31:03 CET 2012ChemnitzGfA-FrühjahrskongressGestaltung technisch-sozialer Vernetzung in der Arbeitsorganisation: Untersuchung zur Nutzerakzeptanz von RFID-Technologie20112011 conferator itegpub mygroup myown networking rfid social venus