@book{balbymarinho2012recommender, abstract = {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.}, added-at = {2012-02-14T08:29:50.000+0100}, author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.}, biburl = {http://www.bibsonomy.org/bibtex/287d6883ebd98e8810be45d7e7e4ade96/hotho}, interhash = {0bb7f0588cd690d67cc73e219a3a24fa}, intrahash = {87d6883ebd98e8810be45d7e7e4ade96}, isbn = {978-1-4614-1893-1}, keywords = {tagging social recommender myown folksonomy collaborative bookmarking 2012}, month = feb, publisher = {Springer}, series = {SpringerBriefs in Electrical and Computer Engineering}, title = {Recommender Systems for Social Tagging Systems}, url = {http://www.springer.com/computer/database+management+%26+information+retrieval/book/978-1-4614-1893-1}, year = 2012 } @book{balbymarinho2012recommender, abstract = {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.}, added-at = {2012-02-13T12:52:23.000+0100}, author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.}, biburl = {http://www.bibsonomy.org/bibtex/287d6883ebd98e8810be45d7e7e4ade96/jaeschke}, interhash = {0bb7f0588cd690d67cc73e219a3a24fa}, intrahash = {87d6883ebd98e8810be45d7e7e4ade96}, isbn = {978-1-4614-1893-1}, keywords = {tagging social recommender myown folksonomy collaborative bookmarking 2012}, month = feb, publisher = {Springer}, series = {SpringerBriefs in Electrical and Computer Engineering}, title = {Recommender Systems for Social Tagging Systems}, url = {http://www.springer.com/computer/database+management+%26+information+retrieval/book/978-1-4614-1893-1}, year = 2012 } @inproceedings{Cattuto.2008, abstract = {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.}, added-at = {2012-02-11T19:24:16.000+0100}, address = {Berlin [u.a.]}, author = {Cattuto, Ciro and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, biburl = {http://www.bibsonomy.org/bibtex/272e33772dff2bb6fe5cac32876e0e2da/peter.b825}, booktitle = {The Semantic Web - ISWC 2008}, doi = {10.1007/978-3-540-88564-1_39}, editor = {Sheth, Amit and Staab, Steffen and Dean, Mike and Paolucci, Massimo and Maynard, Diana and Finin, Timothy and Thirunarayan, Krishnaprasad}, interhash = {b44538648cfd476d6c94e30bc6626c86}, intrahash = {72e33772dff2bb6fe5cac32876e0e2da}, isbn = {978-3-540-88564-1}, keywords = {Social Tagging;Semantic Web}, pages = {615--631}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Semantic Grounding of Tag Relatedness in Social Bookmarking Systems}, url = {http://dx.doi.org/10.1007/978-3-540-88564-1_39}, volume = 5318, year = 2008 } @inproceedings{Krause.2008, abstract = {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.}, added-at = {2012-02-11T19:24:16.000+0100}, address = {Berlin [u.a.]}, author = {Krause, Beate and Hotho, Andreas and Stumme, Gerd}, biburl = {http://www.bibsonomy.org/bibtex/21d56ef654ef3a20214665b5555bca2f3/peter.b825}, booktitle = {Advances in Information Retrieval}, doi = {10.1007/978-3-540-78646-7_12}, editor = {Macdonald, Craig and Ounis, Iadh and Plachouras, Vassilis and Ruthven, Ian and White, Ryen}, interhash = {37598733b747093d97a0840a11beebf5}, intrahash = {1d56ef654ef3a20214665b5555bca2f3}, isbn = {978-3-540-78645-0}, keywords = {imported}, pages = {101--113}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {A Comparison of Social Bookmarking with Traditional Search}, url = {http://dx.doi.org/10.1007/978-3-540-78646-7_12}, volume = 4956, year = 2008 } @inproceedings{Hotho.2006, abstract = {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.}, added-at = {2012-02-11T19:24:16.000+0100}, address = {Berlin [u.a.]}, author = {Hotho, Andreas and J{\"a}schke, Robert and Schmitz, Christoph and Stumme, Gerd}, biburl = {http://www.bibsonomy.org/bibtex/2a8c40df83b1d7a3a25b0000afa66ecb2/peter.b825}, booktitle = {The Semantic Web: Research and Applications}, doi = {10.1007/11762256_31}, editor = {Sure, York and Domingue, John}, interhash = {882bd942131c6c303bdc9c4732287ae9}, intrahash = {a8c40df83b1d7a3a25b0000afa66ecb2}, isbn = {978-3-540-34544-2}, keywords = {imported}, pages = {411--426}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Information Retrieval in Folksonomies: Search and Ranking}, url = {http://dx.doi.org/10.1007/11762256_31}, volume = 4011, year = 2006 } @inproceedings{Illig.2007, added-at = {2012-02-11T19:24:16.000+0100}, address = {Berlin}, author = {Illig, Jens and Hotho, Andreas and J{\"a}schke, Robert and Stumme, Gerd}, biburl = {http://www.bibsonomy.org/bibtex/27a2aae07b168df7f70118a65ff0d73bf/peter.b825}, booktitle = {Knowledge processing and data analysis}, editor = {Wolff, Karl Erich and Palchunov, Dmitry E. and Zagoruiko, Nikolay G. and Andelfinger, Urs}, interhash = {e32f72a1cff81bad834821c1bf702d60}, intrahash = {7a2aae07b168df7f70118a65ff0d73bf}, isbn = {978-3-642-22139-2}, keywords = {imported}, pages = {136--149}, publisher = {Springer}, series = {Lecture notes in artificial intelligence}, title = {A comparison of content-based tag recommendations in folksonomy systems}, volume = 6581, year = 2007 } @incollection{jaeschke2012challenges, abstract = {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.}, added-at = {2012-02-09T09:26:57.000+0100}, address = {Berlin/Heidelberg}, affiliation = {Knowledge & Data Engineering Group, University of Kassel, Wilhelmshöher Allee 73, 34121 Kassel, Germany}, author = {Jäschke, Robert and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd}, biburl = {http://www.bibsonomy.org/bibtex/27d41d332cccc3e7ba8e7dadfb7996337/hotho}, booktitle = {Recommender Systems for the Social Web}, doi = {10.1007/978-3-642-25694-3_3}, editor = {Pazos Arias, José J. and Fernández Vilas, Ana and Díaz Redondo, Rebeca P.}, interhash = {75b1a6f54ef54d0126d0616b5bf77563}, intrahash = {7d41d332cccc3e7ba8e7dadfb7996337}, isbn = {978-3-642-25694-3}, keywords = {2012 bookmarking challenge collaborative dc09 discovery folksonomy myown recommender rsdc08 social tagging}, pages = {65--87}, publisher = {Springer}, series = {Intelligent Systems Reference Library}, title = {Challenges in Tag Recommendations for Collaborative Tagging Systems}, url = {http://dx.doi.org/10.1007/978-3-642-25694-3_3}, volume = 32, year = 2012 } @incollection{jaeschke2012challenges, abstract = {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.}, added-at = {2012-02-06T14:59:32.000+0100}, address = {Berlin/Heidelberg}, affiliation = {Knowledge & Data Engineering Group, University of Kassel, Wilhelmshöher Allee 73, 34121 Kassel, Germany}, author = {Jäschke, Robert and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd}, biburl = {http://www.bibsonomy.org/bibtex/27d41d332cccc3e7ba8e7dadfb7996337/stumme}, booktitle = {Recommender Systems for the Social Web}, doi = {10.1007/978-3-642-25694-3_3}, editor = {Pazos Arias, José J. and Fernández Vilas, Ana and Díaz Redondo, Rebeca P.}, interhash = {75b1a6f54ef54d0126d0616b5bf77563}, intrahash = {7d41d332cccc3e7ba8e7dadfb7996337}, isbn = {978-3-642-25694-3}, keywords = {2012 bookmarking challenge collaborative dc09 discovery folksonomy myown recommender rsdc08 social tagging}, pages = {65--87}, publisher = {Springer}, series = {Intelligent Systems Reference Library}, title = {Challenges in Tag Recommendations for Collaborative Tagging Systems}, url = {http://dx.doi.org/10.1007/978-3-642-25694-3_3}, volume = 32, year = 2012 } @incollection{jaeschke2012challenges, abstract = {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.}, added-at = {2012-02-06T14:04:20.000+0100}, address = {Berlin/Heidelberg}, affiliation = {Knowledge & Data Engineering Group, University of Kassel, Wilhelmshöher Allee 73, 34121 Kassel, Germany}, author = {Jäschke, Robert and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd}, biburl = {http://www.bibsonomy.org/bibtex/27d41d332cccc3e7ba8e7dadfb7996337/dbenz}, booktitle = {Recommender Systems for the Social Web}, doi = {10.1007/978-3-642-25694-3_3}, editor = {Pazos Arias, José J. and Fernández Vilas, Ana and Díaz Redondo, Rebeca P.}, interhash = {75b1a6f54ef54d0126d0616b5bf77563}, intrahash = {7d41d332cccc3e7ba8e7dadfb7996337}, isbn = {978-3-642-25694-3}, keywords = {2012 challenge collaborative recommendation tagging}, pages = {65--87}, publisher = {Springer}, series = {Intelligent Systems Reference Library}, title = {Challenges in Tag Recommendations for Collaborative Tagging Systems}, url = {http://dx.doi.org/10.1007/978-3-642-25694-3_3}, volume = 32, year = 2012 } @incollection{jaeschke2012challenges, abstract = {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.}, added-at = {2012-02-06T14:02:41.000+0100}, address = {Berlin/Heidelberg}, affiliation = {Knowledge & Data Engineering Group, University of Kassel, Wilhelmshöher Allee 73, 34121 Kassel, Germany}, author = {Jäschke, Robert and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd}, biburl = {http://www.bibsonomy.org/bibtex/2b645572f4c635b2d674126f26d4303d6/sdo}, booktitle = {Recommender Systems for the Social Web}, doi = {10.1007/978-3-642-25694-3_3}, editor = {Kacprzyk, Janusz and Jain, Lakhmi C.}, interhash = {75b1a6f54ef54d0126d0616b5bf77563}, intrahash = {b645572f4c635b2d674126f26d4303d6}, isbn = {978-3-642-25694-3}, keywords = {challenge collaborative recommendation system tagging}, pages = {65--87}, publisher = {Springer}, series = {Intelligent Systems Reference Library}, title = {Challenges in Tag Recommendations for Collaborative Tagging Systems}, url = {http://dx.doi.org/10.1007/978-3-642-25694-3_3}, volume = 32, year = 2012 } @incollection{jaeschke2012challenges, abstract = {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.}, added-at = {2012-02-06T13:47:57.000+0100}, address = {Berlin/Heidelberg}, affiliation = {Knowledge & Data Engineering Group, University of Kassel, Wilhelmshöher Allee 73, 34121 Kassel, Germany}, author = {Jäschke, Robert and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd}, biburl = {http://www.bibsonomy.org/bibtex/27d41d332cccc3e7ba8e7dadfb7996337/jaeschke}, booktitle = {Recommender Systems for the Social Web}, doi = {10.1007/978-3-642-25694-3_3}, editor = {Pazos Arias, José J. and Fernández Vilas, Ana and Díaz Redondo, Rebeca P.}, interhash = {75b1a6f54ef54d0126d0616b5bf77563}, intrahash = {7d41d332cccc3e7ba8e7dadfb7996337}, isbn = {978-3-642-25694-3}, keywords = {2012 bookmarking challenge collaborative dc09 discovery folksonomy myown recommender rsdc08 social tagging}, pages = {65--87}, publisher = {Springer}, series = {Intelligent Systems Reference Library}, title = {Challenges in Tag Recommendations for Collaborative Tagging Systems}, url = {http://dx.doi.org/10.1007/978-3-642-25694-3_3}, volume = 32, year = 2012 } @article{journals/insk/KrauseLHRS12, added-at = {2012-02-02T00:00:00.000+0100}, author = {Krause, Beate and Lerch, Hana and Hotho, Andreas and Roßnagel, Alexander and Stumme, Gerd}, biburl = {http://www.bibsonomy.org/bibtex/2df97de393d3421ef2e20384ddde16ab1/dblp}, ee = {http://dx.doi.org/10.1007/s00287-010-0485-8}, interhash = {3fca17b13ee1c002f41d3a2a4594b3e2}, intrahash = {df97de393d3421ef2e20384ddde16ab1}, journal = {Informatik Spektrum}, keywords = {dblp}, number = 1, pages = {12-23}, title = {Datenschutz im Web 2.0 am Beispiel des sozialen Tagging-Systems BibSonomy.}, url = {http://dblp.uni-trier.de/db/journals/insk/insk35.html#KrauseLHRS12}, volume = 35, year = 2012 } @inproceedings{BAKHSS:11, added-at = {2012-02-01T09:08:16.000+0100}, address = {Chemnitz}, author = {Behrenbruch, Kay and Atzmueller, Martin and Kniewel, Romy and Hoberg, Sebastian and Stumme, Gerd and Schmidt, Ludger}, biburl = {http://www.bibsonomy.org/bibtex/2addbaaba7aec8360e23284c849e216ad/hoberg}, booktitle = {GfA-Frühjahrskongress}, interhash = {bb1435b451f54abf143ea892375abf55}, intrahash = {addbaaba7aec8360e23284c849e216ad}, keywords = {iteg itegpub mms mmspub}, title = {Gestaltung technisch-sozialer Vernetzung in der Arbeitsorganisation: Untersuchung zur Nutzerakzeptanz von RFID-Technologie}, year = 2011 } @inproceedings{mitzlaff2011community, added-at = {2012-01-30T09:27:21.000+0100}, author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, biburl = {http://www.bibsonomy.org/bibtex/20f45e870093c053e6f41f54c14bda46b/folke}, booktitle = {Analysis of Social Media and Ubiquitous Data}, interhash = {1ef065a81ed836dfd31fcc4cd4da133b}, intrahash = {0f45e870093c053e6f41f54c14bda46b}, keywords = {2011 assessment community myown}, series = {LNAI}, title = {{Community Assessment using Evidence Networks}}, volume = 6904, year = 2011 } @inproceedings{conf/socialcom/MacekAS11, added-at = {2012-01-30T00:00:00.000+0100}, author = {Macek, Bjoern Elmar and Atzmueller, Martin and Stumme, Gerd}, biburl = {http://www.bibsonomy.org/bibtex/20df5b9113afb1e74415e0fd652e4cac8/dblp}, booktitle = {SocialCom/PASSAT}, crossref = {conf/socialcom/2011}, ee = {http://doi.ieeecomputersociety.org/10.1109/PASSAT/SocialCom.2011.40}, interhash = {93511ed34197130ddf2ed9585b068053}, intrahash = {0df5b9113afb1e74415e0fd652e4cac8}, isbn = {978-1-4577-1931-8}, keywords = {dblp}, pages = {250-257}, publisher = {IEEE}, title = {Profile Mining in CVS-Logs and Face-to-Face Contacts for Recommending Software Developers.}, url = {http://dblp.uni-trier.de/db/conf/socialcom/socialcom2011.html#MacekAS11}, year = 2011 } @inproceedings{bullock2011privacyaware, abstract = {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.}, acmid = {2024306}, added-at = {2012-01-20T09:29:21.000+0100}, address = {New York, NY, USA}, articleno = {15}, author = {Bullock, Beate Navarro and Lerch, Hana and Ro\ssnagel, Alexander and Hotho, Andreas and Stumme, Gerd}, biburl = {http://www.bibsonomy.org/bibtex/200a8f31185a34957eb16d500d7d51398/hotho}, booktitle = {Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies}, description = {Privacy-aware spam detection in social bookmarking systems}, doi = {10.1145/2024288.2024306}, interhash = {7a2d6a35c124ea0fe31c962f8f150916}, intrahash = {00a8f31185a34957eb16d500d7d51398}, isbn = {978-1-4503-0732-1}, keywords = {2011 datamining detection myown privacy spam web20}, location = {Graz, Austria}, numpages = {8}, pages = {15:1--15:8}, publisher = {ACM}, series = {i-KNOW '11}, title = {Privacy-aware spam detection in social bookmarking systems}, url = {http://doi.acm.org/10.1145/2024288.2024306}, year = 2011 } @article{Atzmueller2011a, abstract = {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.}, added-at = {2012-01-19T15:57:22.000+0100}, address = {München}, author = {Atzmueller, Martin and Benz, Dominik and Doerfel, Stephan and Hotho, Andreas and Jäschke, Robert and Macek, Bjoern Elmar and Mitzlaff, Folke and Scholz, Christoph and Stumme, Gerd}, biburl = {http://www.bibsonomy.org/bibtex/2b96a6cf5d9999ca9063b7d7cd229e50d/enitsirhc}, doi = {10.1524/itit.2011.0631}, interhash = {e57bff1f73b74e6f1fe79e4b40956c35}, intrahash = {b96a6cf5d9999ca9063b7d7cd229e50d}, issn = {1611-2776}, journal = {Information Technology}, keywords = {rfid social 2011 myown computing conference ubiquitous network conferator}, month = may, number = 3, pages = {101--107}, publisher = {Oldenbourg Wissenschaftsverlag}, title = {Enhancing Social Interactions at Conferences}, url = {http://www.oldenbourg-link.com/doi/abs/10.1524/itit.2011.0631}, vgwort = {22}, volume = 53, year = 2011 } @article{stumme-2002, abstract = {We introduce the notion of iceberg concept lattices and show their use in knowledge discovery in databases. Iceberg lattices are a conceptual clustering method, which is well suited for analyzing very large databases. They also serve as a condensed representation of frequent itemsets, as starting point for computing bases of association rules, and as a visualization method for association rules. Iceberg concept lattices are based on the theory of Formal Concept Analysis, a mathematical theory with applications in data analysis, information retrieval, and knowledge discovery. We present a new algorithm called Titanic for computing (iceberg) concept lattices. It is based on data mining techniques with a level-wise approach. In fact, Titanic can be used for a more general problem: Computing arbitrary closure systems when the closure operator comes along with a so-called weight function. The use of weight functions for computing closure systems has not been discussed in the literature up to now. Applications providing such a weight function include association rule mining, functional dependencies in databases, conceptual clustering, and ontology engineering. The algorithm is experimentally evaluated and compared with Ganter's Next-Closure algorithm. The evaluation shows an important gain in efficiency, especially for weakly correlated data.}, added-at = {2012-01-19T15:40:13.000+0100}, author = {Stumme, Gerd and Taouil, Rafik and Bastide, Yves and Pasquier, Nicolas and Lakhal, Lotfi}, biburl = {http://www.bibsonomy.org/bibtex/28f2140b4f51fbbd4f477c5bcc904ae52/juergen.mueller}, doi = {10.1016/S0169-023X(02)00057-5}, interhash = {d500ac8a249ca8bf0fb05f382799d48f}, intrahash = {8f2140b4f51fbbd4f477c5bcc904ae52}, issn = {0169-023X}, journal = {Data \& Knowledge Engineering}, keywords = {2002 KDD Titanic article}, month = aug, number = 2, pages = {189 - 222}, title = {Computing iceberg concept lattices with Titanic}, volume = 42, year = 2002 } @article{benz2010social, abstract = {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.}, added-at = {2012-01-19T12:34:45.000+0100}, address = {Berlin / Heidelberg}, author = {Benz, Dominik and Hotho, Andreas and Jäschke, Robert and Krause, Beate and Mitzlaff, Folke and Schmitz, Christoph and Stumme, Gerd}, biburl = {http://www.bibsonomy.org/bibtex/2c9437d5ec56ba949f533aeec00f571e3/nosebrain}, doi = {10.1007/s00778-010-0208-4}, interhash = {57fe43734b18909a24bf5bf6608d2a09}, intrahash = {c9437d5ec56ba949f533aeec00f571e3}, issn = {1066-8888}, journal = {The VLDB Journal}, keywords = {bibsonomy bookmark publication sharing social system}, month = dec, number = 6, pages = {849--875}, publisher = {Springer}, title = {The Social Bookmark and Publication Management System BibSonomy}, url = {http://www.kde.cs.uni-kassel.de/pub/pdf/benz2010social.pdf}, volume = 19, year = 2010 } @inproceedings{DBLP:conf/pkdd/ADHSS11, added-at = {2012-01-19T10:29:03.000+0100}, author = {Scholz, Christoph and Doerfel, Stephan and Atzmueller, Martin and Hotho, Andreas and Stumme, Gerd}, biburl = {http://www.bibsonomy.org/bibtex/2deec7e33ba811cd4dfacb29e6dc0fb9c/hotho}, booktitle = {ECML/PKDD (3)}, description = {accepted}, interhash = {d81c55cdcdf8ee331595bbb4d6fd51d6}, intrahash = {deec7e33ba811cd4dfacb29e6dc0fb9c}, keywords = {2011 localization myown rfid}, note = {misc = 27}, pages = {129-144}, title = {Resource-Aware On-Line RFID Localization Using Proximity Data}, vgwort = {27}, year = 2011 }