Dr. Folke Mitzlaff

Hinweis: Folke Mitzlaff ist seit dem 30.10.2013 nicht mehr im Fachgebiet beschäftigt.

Dr. Folke Mitzlaff
Mail: mitzlaff@cs.uni-kassel.de

mitzlaff at cs.uni-kassel.de

Projekte

• Nameling – An intelligent name browser
• BibSonomy – The Blue Social Bookmark and Publication Sharing System
• 15th Discovery Challenge organisiert in Verbindung mit der ECML PKDD 2013, 23. – 27. September, 2013, Prag, Tschechien

Veröffentlichungen

2014

• 1.

  Mitzlaff, F., Atzmueller, M., Hotho, A., Stumme, G.: {The Social Distributional Hypothesis}. Journal of Social Network Analysis and Mining. 4, (2014).

  BibTeXEndNote

  @article{MAHS:14,
  author = {Mitzlaff, Folke and Atzmueller, Martin and Hotho, Andreas and Stumme, Gerd},
  journal = {Journal of Social Network Analysis and Mining},
  keywords = {itegpub},
  number = 216,
  title = {{The Social Distributional Hypothesis}},
  volume = 4,
  year = 2014
  }
  

  %0 Journal Article
  %1 MAHS:14
  %A Mitzlaff, Folke
  %A Atzmueller, Martin
  %A Hotho, Andreas
  %A Stumme, Gerd
  %D 2014
  %J Journal of Social Network Analysis and Mining
  %N 216
  %T {The Social Distributional Hypothesis}
  %V 4
  

2013

• 1.

  Mitzlaff, F.: Name Me If You Can(!) - Leveraging Networks of Given Names. In: Proceedings from Sunbelt XXXIII (2013).

  BibTeXEndNote

  @inproceedings{mitzlaff2013leveraging,
  author = {Mitzlaff, Folke},
  booktitle = {Proceedings from Sunbelt XXXIII},
  keywords = {nameling},
  title = {Name Me If You Can(!) - Leveraging Networks of Given Names},
  year = 2013
  }
  

  %0 Conference Paper
  %1 mitzlaff2013leveraging
  %A Mitzlaff, Folke
  %B Proceedings from Sunbelt XXXIII
  %D 2013
  %T Name Me If You Can(!) - Leveraging Networks of Given Names
  

• 1.

  Mitzlaff, F., Stumme, G.: Onomastics 2.0 - The Power of Social Co-Occurrences, http://arxiv.org/abs/1303.0484, (2013).

  URLBibTeXEndNote

  Onomastics is "the science or study of the origin and forms of proper names of persons or places." ["Onomastics". Merriam-Webster.com, 2013. http://www.merriam-webster.com (11 February 2013)]. Especially personal names play an important role in daily life, as all over the world future parents are facing the task of finding a suitable given name for their child. This choice is influenced by different factors, such as the social context, language, cultural background and, in particular, personal taste. With the rise of the Social Web and its applications, users more and more interact digitally and participate in the creation of heterogeneous, distributed, collaborative data collections. These sources of data also reflect current and new naming trends as well as new emerging interrelations among names. The present work shows, how basic approaches from the field of social network analysis and information retrieval can be applied for discovering relations among names, thus extending Onomastics by data mining techniques. The considered approach starts with building co-occurrence graphs relative to data from the Social Web, respectively for given names and city names. As a main result, correlations between semantically grounded similarities among names (e.g., geographical distance for city names) and structural graph based similarities are observed. The discovered relations among given names are the foundation of "nameling" [http://nameling.net], a search engine and academic research platform for given names which attracted more than 30,000 users within four months, underpinningthe relevance of the proposed methodology.

  @misc{mitzlaff2013onomastics,
  abstract = {Onomastics is "the science or study of the origin and forms of proper names of persons or places." ["Onomastics". Merriam-Webster.com, 2013. http://www.merriam-webster.com (11 February 2013)]. Especially personal names play an important role in daily life, as all over the world future parents are facing the task of finding a suitable given name for their child. This choice is influenced by different factors, such as the social context, language, cultural background and, in particular, personal taste. With the rise of the Social Web and its applications, users more and more interact digitally and participate in the creation of heterogeneous, distributed, collaborative data collections. These sources of data also reflect current and new naming trends as well as new emerging interrelations among names. The present work shows, how basic approaches from the field of social network analysis and information retrieval can be applied for discovering relations among names, thus extending Onomastics by data mining techniques. The considered approach starts with building co-occurrence graphs relative to data from the Social Web, respectively for given names and city names. As a main result, correlations between semantically grounded similarities among names (e.g., geographical distance for city names) and structural graph based similarities are observed. The discovered relations among given names are the foundation of "nameling" [http://nameling.net], a search engine and academic research platform for given names which attracted more than 30,000 users within four months, underpinningthe relevance of the proposed methodology.},
  author = {Mitzlaff, Folke and Stumme, Gerd},
  keywords = {nameling},
  note = {cite arxiv:1303.0484Comment: Historically, this is the first paper on the analysis of names in the context of the name search engine 'nameling'. arXiv admin note: text overlap with arXiv:1302.4412},
  title = {Onomastics 2.0 - The Power of Social Co-Occurrences},
  year = 2013
  }
  

  %0 Generic
  %1 mitzlaff2013onomastics
  %A Mitzlaff, Folke
  %A Stumme, Gerd
  %D 2013
  %T Onomastics 2.0 - The Power of Social Co-Occurrences
  %U http://arxiv.org/abs/1303.0484
  %X Onomastics is "the science or study of the origin and forms of proper names of persons or places." ["Onomastics". Merriam-Webster.com, 2013. http://www.merriam-webster.com (11 February 2013)]. Especially personal names play an important role in daily life, as all over the world future parents are facing the task of finding a suitable given name for their child. This choice is influenced by different factors, such as the social context, language, cultural background and, in particular, personal taste. With the rise of the Social Web and its applications, users more and more interact digitally and participate in the creation of heterogeneous, distributed, collaborative data collections. These sources of data also reflect current and new naming trends as well as new emerging interrelations among names. The present work shows, how basic approaches from the field of social network analysis and information retrieval can be applied for discovering relations among names, thus extending Onomastics by data mining techniques. The considered approach starts with building co-occurrence graphs relative to data from the Social Web, respectively for given names and city names. As a main result, correlations between semantically grounded similarities among names (e.g., geographical distance for city names) and structural graph based similarities are observed. The discovered relations among given names are the foundation of "nameling" [http://nameling.net], a search engine and academic research platform for given names which attracted more than 30,000 users within four months, underpinningthe relevance of the proposed methodology.
  

• 1.

  Mitzlaff, F., Atzmueller, M., Stumme, G., Hotho, A.: Semantics of User Interaction in Social Media. In: Ghoshal, G., Poncela-Casasnovas, J., and Tolksdorf, R. (eds.) Complex Networks IV. pp. 13–25. Springer Berlin Heidelberg (2013). https://doi.org/10.1007/978-3-642-36844-8_2.

  URLBibTeXEndNote

  In ubiquitous and social web applications, there are different user traces, for example, produced explicitly by ”tweeting” via twitter or implicitly, when the corresponding activities are logged within the application’s internal databases and log files.

  @incollection{mitzlaff2013semantics,
  abstract = {In ubiquitous and social web applications, there are different user traces, for example, produced explicitly by ”tweeting” via twitter or implicitly, when the corresponding activities are logged within the application’s internal databases and log files.},
  author = {Mitzlaff, Folke and Atzmueller, Martin and Stumme, Gerd and Hotho, Andreas},
  booktitle = {Complex Networks IV},
  editor = {Ghoshal, Gourab and Poncela-Casasnovas, Julia and Tolksdorf, Robert},
  keywords = {networks},
  pages = {13-25},
  publisher = {Springer Berlin Heidelberg},
  series = {Studies in Computational Intelligence},
  title = {Semantics of User Interaction in Social Media},
  volume = 476,
  year = 2013
  }
  

  %0 Book Section
  %1 mitzlaff2013semantics
  %A Mitzlaff, Folke
  %A Atzmueller, Martin
  %A Stumme, Gerd
  %A Hotho, Andreas
  %B Complex Networks IV
  %D 2013
  %E Ghoshal, Gourab
  %E Poncela-Casasnovas, Julia
  %E Tolksdorf, Robert
  %I Springer Berlin Heidelberg
  %P 13-25
  %R 10.1007/978-3-642-36844-8_2
  %T Semantics of User Interaction in Social Media
  %U https://www.kde.cs.uni-kassel.de/pub/pdf/mitzlaff2013semantics.pdf
  %V 476
  %X In ubiquitous and social web applications, there are different user traces, for example, produced explicitly by ”tweeting” via twitter or implicitly, when the corresponding activities are logged within the application’s internal databases and log files.
  %@ 978-3-642-36843-1
  

• 1.

  Mitzlaff, F., Atzmueller, M., Benz, D., Hotho, A., Stumme, G.: User-Relatedness and Community Structure in Social Interaction Networks, http://arxiv.org/abs/1309.3888, (2013).

  URLBibTeXEndNote

  With social media and the according social and ubiquitous applications finding their way into everyday life, there is a rapidly growing amount of user generated content yielding explicit and implicit network structures. We consider social activities and phenomena as proxies for user relatedness. Such activities are represented in so-called social interaction networks or evidence networks, with different degrees of explicitness. We focus on evidence networks containing relations on users, which are represented by connections between individual nodes. Explicit interaction networks are then created by specific user actions, for example, when building a friend network. On the other hand, more implicit networks capture user traces or evidences of user actions as observed in Web portals, blogs, resource sharing systems, and many other social services. These implicit networks can be applied for a broad range of analysis methods instead of using expensive gold-standard information. In this paper, we analyze different properties of a set of networks in social media. We show that there are dependencies and correlations between the networks. These allow for drawing reciprocal conclusions concerning pairs of networks, based on the assessment of structural correlations and ranking interchangeability. Additionally, we show how these inter-network correlations can be used for assessing the results of structural analysis techniques, e.g., community mining methods.

  @misc{mitzlaff2013userrelatedness,
  abstract = {With social media and the according social and ubiquitous applications finding their way into everyday life, there is a rapidly growing amount of user generated content yielding explicit and implicit network structures. We consider social activities and phenomena as proxies for user relatedness. Such activities are represented in so-called social interaction networks or evidence networks, with different degrees of explicitness. We focus on evidence networks containing relations on users, which are represented by connections between individual nodes. Explicit interaction networks are then created by specific user actions, for example, when building a friend network. On the other hand, more implicit networks capture user traces or evidences of user actions as observed in Web portals, blogs, resource sharing systems, and many other social services. These implicit networks can be applied for a broad range of analysis methods instead of using expensive gold-standard information. In this paper, we analyze different properties of a set of networks in social media. We show that there are dependencies and correlations between the networks. These allow for drawing reciprocal conclusions concerning pairs of networks, based on the assessment of structural correlations and ranking interchangeability. Additionally, we show how these inter-network correlations can be used for assessing the results of structural analysis techniques, e.g., community mining methods.},
  author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd},
  keywords = {networks},
  note = {cite arxiv:1309.3888},
  title = {User-Relatedness and Community Structure in Social Interaction Networks},
  year = 2013
  }
  

  %0 Generic
  %1 mitzlaff2013userrelatedness
  %A Mitzlaff, Folke
  %A Atzmueller, Martin
  %A Benz, Dominik
  %A Hotho, Andreas
  %A Stumme, Gerd
  %D 2013
  %T User-Relatedness and Community Structure in Social Interaction Networks
  %U http://arxiv.org/abs/1309.3888
  %X With social media and the according social and ubiquitous applications finding their way into everyday life, there is a rapidly growing amount of user generated content yielding explicit and implicit network structures. We consider social activities and phenomena as proxies for user relatedness. Such activities are represented in so-called social interaction networks or evidence networks, with different degrees of explicitness. We focus on evidence networks containing relations on users, which are represented by connections between individual nodes. Explicit interaction networks are then created by specific user actions, for example, when building a friend network. On the other hand, more implicit networks capture user traces or evidences of user actions as observed in Web portals, blogs, resource sharing systems, and many other social services. These implicit networks can be applied for a broad range of analysis methods instead of using expensive gold-standard information. In this paper, we analyze different properties of a set of networks in social media. We show that there are dependencies and correlations between the networks. These allow for drawing reciprocal conclusions concerning pairs of networks, based on the assessment of structural correlations and ranking interchangeability. Additionally, we show how these inter-network correlations can be used for assessing the results of structural analysis techniques, e.g., community mining methods.
  

• 1.

  Mitzlaff, F., Stumme, G.: Recommending Given Names, http://arxiv.org/abs/1302.4412, (2013).

  URLBibTeXEndNote

  All over the world, future parents are facing the task of finding a suitable given name for their child. This choice is influenced by different factors, such as the social context, language, cultural background and especially personal taste. Although this task is omnipresent, little research has been conducted on the analysis and application of interrelations among given names from a data mining perspective. The present work tackles the problem of recommending given names, by firstly mining for inter-name relatedness in data from the Social Web. Based on these results, the name search engine "Nameling" was built, which attracted more than 35,000 users within less than six months, underpinning the relevance of the underlying recommendation task. The accruing usage data is then used for evaluating different state-of-the-art recommendation systems, as well our new \NR algorithm which we adopted from our previous work on folksonomies and which yields the best results, considering the trade-off between prediction accuracy and runtime performance as well as its ability to generate personalized recommendations. We also show, how the gathered inter-name relationships can be used for meaningful result diversification of PageRank-based recommendation systems. As all of the considered usage data is made publicly available, the present work establishes baseline results, encouraging other researchers to implement advanced recommendation systems for given names.

  @misc{mitzlaff2013recommending,
  abstract = {All over the world, future parents are facing the task of finding a suitable given name for their child. This choice is influenced by different factors, such as the social context, language, cultural background and especially personal taste. Although this task is omnipresent, little research has been conducted on the analysis and application of interrelations among given names from a data mining perspective. The present work tackles the problem of recommending given names, by firstly mining for inter-name relatedness in data from the Social Web. Based on these results, the name search engine "Nameling" was built, which attracted more than 35,000 users within less than six months, underpinning the relevance of the underlying recommendation task. The accruing usage data is then used for evaluating different state-of-the-art recommendation systems, as well our new \NR algorithm which we adopted from our previous work on folksonomies and which yields the best results, considering the trade-off between prediction accuracy and runtime performance as well as its ability to generate personalized recommendations. We also show, how the gathered inter-name relationships can be used for meaningful result diversification of PageRank-based recommendation systems. As all of the considered usage data is made publicly available, the present work establishes baseline results, encouraging other researchers to implement advanced recommendation systems for given names.},
  author = {Mitzlaff, Folke and Stumme, Gerd},
  keywords = {nameling},
  note = {cite arxiv:1302.4412Comment: Baseline results for the ECML PKDD Discovery Challenge 2013},
  title = {Recommending Given Names},
  year = 2013
  }
  

  %0 Generic
  %1 mitzlaff2013recommending
  %A Mitzlaff, Folke
  %A Stumme, Gerd
  %D 2013
  %T Recommending Given Names
  %U http://arxiv.org/abs/1302.4412
  %X All over the world, future parents are facing the task of finding a suitable given name for their child. This choice is influenced by different factors, such as the social context, language, cultural background and especially personal taste. Although this task is omnipresent, little research has been conducted on the analysis and application of interrelations among given names from a data mining perspective. The present work tackles the problem of recommending given names, by firstly mining for inter-name relatedness in data from the Social Web. Based on these results, the name search engine "Nameling" was built, which attracted more than 35,000 users within less than six months, underpinning the relevance of the underlying recommendation task. The accruing usage data is then used for evaluating different state-of-the-art recommendation systems, as well our new \NR algorithm which we adopted from our previous work on folksonomies and which yields the best results, considering the trade-off between prediction accuracy and runtime performance as well as its ability to generate personalized recommendations. We also show, how the gathered inter-name relationships can be used for meaningful result diversification of PageRank-based recommendation systems. As all of the considered usage data is made publicly available, the present work establishes baseline results, encouraging other researchers to implement advanced recommendation systems for given names.
  

2012

• 1.

  Mitzlaff, F., Stumme, G.: Ranking Given Names. In: Marathe, M. and Contractor, N. (eds.) Proceedings of the 1st ASE International Conference on Social Informatics. pp. 185–191. IEEE computer society (2012).

  BibTeXEndNote

  @inproceedings{mitzlaff2012ranking,
  author = {Mitzlaff, Folke and Stumme, Gerd},
  booktitle = {Proceedings of the 1st ASE International Conference on Social Informatics},
  editor = {Marathe, Madhav and Contractor, Noshir},
  keywords = {nameling},
  pages = {185-191},
  publisher = {IEEE computer society},
  title = {Ranking Given Names},
  year = 2012
  }
  

  %0 Conference Paper
  %1 mitzlaff2012ranking
  %A Mitzlaff, Folke
  %A Stumme, Gerd
  %B Proceedings of the 1st ASE International Conference on Social Informatics
  %D 2012
  %E Marathe, Madhav
  %E Contractor, Noshir
  %I IEEE computer society
  %P 185-191
  %T Ranking Given Names
  

• 1.

  Jäschke, R., Hotho, A., Mitzlaff, F., Stumme, G.: Challenges in Tag Recommendations for Collaborative Tagging Systems. In: Pazos Arias, J.J., Fernández Vilas, A., and Díaz Redondo, R.P. (eds.) Recommender Systems for the Social Web. pp. 65–87. Springer, Berlin/Heidelberg (2012). https://doi.org/10.1007/978-3-642-25694-3_3.

  URLBibTeXEndNote

  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.

  @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.},
  address = {Berlin/Heidelberg},
  author = {Jäschke, Robert and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd},
  booktitle = {Recommender Systems for the Social Web},
  editor = {Pazos Arias, José J. and Fernández Vilas, Ana and Díaz Redondo, Rebeca P.},
  keywords = {recommender},
  pages = {65--87},
  publisher = {Springer},
  series = {Intelligent Systems Reference Library},
  title = {Challenges in Tag Recommendations for Collaborative Tagging Systems},
  volume = 32,
  year = 2012
  }
  

  %0 Book Section
  %1 jaeschke2012challenges
  %A Jäschke, Robert
  %A Hotho, Andreas
  %A Mitzlaff, Folke
  %A Stumme, Gerd
  %B Recommender Systems for the Social Web
  %C Berlin/Heidelberg
  %D 2012
  %E Pazos Arias, José J.
  %E Fernández Vilas, Ana
  %E Díaz Redondo, Rebeca P.
  %I Springer
  %P 65--87
  %R 10.1007/978-3-642-25694-3_3
  %T Challenges in Tag Recommendations for Collaborative Tagging Systems
  %U http://dx.doi.org/10.1007/978-3-642-25694-3_3
  %V 32
  %X 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.
  %@ 978-3-642-25694-3
  

• 1.

  Mitzlaff, F., Stumme, G.: Relatedness of Given Names. Human Journal. 1, 205–217 (2012).

  URLBibTeXEndNote

  As a result of the author's need for help in finding a given namefor the unborn baby, nameling, a search engine for given names, based on data from the ``Social Web'' was born. Within less than six months, more than 35,000 users accessed nameling with more than 300,000 search requests, underpinning the relevance of the underlying research questions. The present work proposes a new approach for discovering relations among given names, based on co-occurrences within Wikipedia. In particular, the task of finding relevant names for a given search query is considered as a ranking task and the performance of different measures of relatedness among given names are evaluated with respect to nameling's actual usage data. We will show that a modification for the PageRank algorithm overcomes limitations imposed by global network characteristics to preferential PageRank computations. By publishing the considered usage data, the research community is stipulated for developing advanced recommendation systems and analyzing influencing factors for the choice of a given name.

  @article{mitzlaff2012relatedness,
  abstract = {As a result of the author's need for help in finding a given namefor the unborn baby, nameling, a search engine for given names, based on data from the ``Social Web'' was born. Within less than six months, more than 35,000 users accessed nameling with more than 300,000 search requests, underpinning the relevance of the underlying research questions. The present work proposes a new approach for discovering relations among given names, based on co-occurrences within Wikipedia. In particular, the task of finding relevant names for a given search query is considered as a ranking task and the performance of different measures of relatedness among given names are evaluated with respect to nameling's actual usage data. We will show that a modification for the PageRank algorithm overcomes limitations imposed by global network characteristics to preferential PageRank computations. By publishing the considered usage data, the research community is stipulated for developing advanced recommendation systems and analyzing influencing factors for the choice of a given name.},
  author = {Mitzlaff, Folke and Stumme, Gerd},
  journal = {Human Journal},
  keywords = {nameling},
  number = 4,
  pages = {205-217},
  publisher = {Academy of Science and Engineering},
  title = {Relatedness of Given Names},
  volume = 1,
  year = 2012
  }
  

  %0 Journal Article
  %1 mitzlaff2012relatedness
  %A Mitzlaff, Folke
  %A Stumme, Gerd
  %D 2012
  %I Academy of Science and Engineering
  %J Human Journal
  %N 4
  %P 205-217
  %T Relatedness of Given Names
  %U https://www.kde.cs.uni-kassel.de/pub/pdf/mitzlaff2012relatedness.pdf
  %V 1
  %X As a result of the author's need for help in finding a given namefor the unborn baby, nameling, a search engine for given names, based on data from the ``Social Web'' was born. Within less than six months, more than 35,000 users accessed nameling with more than 300,000 search requests, underpinning the relevance of the underlying research questions. The present work proposes a new approach for discovering relations among given names, based on co-occurrences within Wikipedia. In particular, the task of finding relevant names for a given search query is considered as a ranking task and the performance of different measures of relatedness among given names are evaluated with respect to nameling's actual usage data. We will show that a modification for the PageRank algorithm overcomes limitations imposed by global network characteristics to preferential PageRank computations. By publishing the considered usage data, the research community is stipulated for developing advanced recommendation systems and analyzing influencing factors for the choice of a given name.
  

• 1.

  Landia, N., Anand, S.S., Hotho, A., Jäschke, R., Doerfel, S., Mitzlaff, F.: Extending FolkRank with content data. In: Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web. pp. 1–8. ACM, Dublin, Ireland (2012). https://doi.org/10.1145/2365934.2365936.

  URLBibTeXEndNote

  Real-world tagging datasets have a large proportion of new/ untagged documents. Few approaches for recommending tags to a user for a document address this new item problem, concentrating instead on artificially created post-core datasets where it is guaranteed that the user as well as the document of each test post is known to the system and already has some tags assigned to it. In order to recommend tags for new documents, approaches are required which model documents not only based on the tags assigned to them in the past (if any), but also the content. In this paper we present a novel adaptation to the widely recognised FolkRank tag recommendation algorithm by including content data. We adapt the FolkRank graph to use word nodes instead of document nodes, enabling it to recommend tags for new documents based on their textual content. Our adaptations make FolkRank applicable to post-core 1 ie. the full real-world tagging datasets and address the new item problem in tag recommendation. For comparison, we also apply and evaluate the same methodology of including content on a simpler tag recommendation algorithm. This results in a less expensive recommender which suggests a combination of user related and document content related tags.

  Including content data into FolkRank shows an improvement over plain FolkRank on full tagging datasets. However, we also observe that our simpler content-aware tag recommender outperforms FolkRank with content data. Our results suggest that an optimisation of the weighting method of FolkRank is required to achieve better results.

  @inproceedings{landia2012extending,
  abstract = {Real-world tagging datasets have a large proportion of new/ untagged documents. Few approaches for recommending tags to a user for a document address this new item problem, concentrating instead on artificially created post-core datasets where it is guaranteed that the user as well as the document of each test post is known to the system and already has some tags assigned to it. In order to recommend tags for new documents, approaches are required which model documents not only based on the tags assigned to them in the past (if any), but also the content. In this paper we present a novel adaptation to the widely recognised FolkRank tag recommendation algorithm by including content data. We adapt the FolkRank graph to use word nodes instead of document nodes, enabling it to recommend tags for new documents based on their textual content. Our adaptations make FolkRank applicable to post-core 1 ie. the full real-world tagging datasets and address the new item problem in tag recommendation. For comparison, we also apply and evaluate the same methodology of including content on a simpler tag recommendation algorithm. This results in a less expensive recommender which suggests a combination of user related and document content related tags.

  Including content data into FolkRank shows an improvement over plain FolkRank on full tagging datasets. However, we also observe that our simpler content-aware tag recommender outperforms FolkRank with content data. Our results suggest that an optimisation of the weighting method of FolkRank is required to achieve better results.},
  address = {New York, NY, USA},
  author = {Landia, Nikolas and Anand, Sarabjot Singh and Hotho, Andreas and Jäschke, Robert and Doerfel, Stephan and Mitzlaff, Folke},
  booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web},
  keywords = {content},
  pages = {1--8},
  publisher = {ACM},
  series = {RSWeb '12},
  title = {Extending FolkRank with content data},
  year = 2012
  }
  

  %0 Conference Paper
  %1 landia2012extending
  %A Landia, Nikolas
  %A Anand, Sarabjot Singh
  %A Hotho, Andreas
  %A Jäschke, Robert
  %A Doerfel, Stephan
  %A Mitzlaff, Folke
  %B Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web
  %C New York, NY, USA
  %D 2012
  %I ACM
  %P 1--8
  %R 10.1145/2365934.2365936
  %T Extending FolkRank with content data
  %U http://doi.acm.org/10.1145/2365934.2365936
  %X Real-world tagging datasets have a large proportion of new/ untagged documents. Few approaches for recommending tags to a user for a document address this new item problem, concentrating instead on artificially created post-core datasets where it is guaranteed that the user as well as the document of each test post is known to the system and already has some tags assigned to it. In order to recommend tags for new documents, approaches are required which model documents not only based on the tags assigned to them in the past (if any), but also the content. In this paper we present a novel adaptation to the widely recognised FolkRank tag recommendation algorithm by including content data. We adapt the FolkRank graph to use word nodes instead of document nodes, enabling it to recommend tags for new documents based on their textual content. Our adaptations make FolkRank applicable to post-core 1 ie. the full real-world tagging datasets and address the new item problem in tag recommendation. For comparison, we also apply and evaluate the same methodology of including content on a simpler tag recommendation algorithm. This results in a less expensive recommender which suggests a combination of user related and document content related tags.

  Including content data into FolkRank shows an improvement over plain FolkRank on full tagging datasets. However, we also observe that our simpler content-aware tag recommender outperforms FolkRank with content data. Our results suggest that an optimisation of the weighting method of FolkRank is required to achieve better results.
  %@ 978-1-4503-1638-5
  

• 1.

  Mitzlaff, F., Stumme, G.: Namelings - Discover Given Name Relatedness Based on Data from the Social Web. In: Aberer, K., Flache, A., Jager, W., Liu, L., Tang, J., and Guéret, C. (eds.) SocInfo. pp. 531–534. Springer (2012).

  URLBibTeXEndNote

  @inproceedings{mitzlaff2012namelings,
  author = {Mitzlaff, Folke and Stumme, Gerd},
  booktitle = {SocInfo},
  crossref = {conf/socinfo/2012},
  editor = {Aberer, Karl and Flache, Andreas and Jager, Wander and Liu, Ling and Tang, Jie and Guéret, Christophe},
  keywords = {nameling},
  pages = {531-534},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Namelings - Discover Given Name Relatedness Based on Data from the Social Web.},
  volume = 7710,
  year = 2012
  }
  

  %0 Conference Paper
  %1 mitzlaff2012namelings
  %A Mitzlaff, Folke
  %A Stumme, Gerd
  %B SocInfo
  %D 2012
  %E Aberer, Karl
  %E Flache, Andreas
  %E Jager, Wander
  %E Liu, Ling
  %E Tang, Jie
  %E Guéret, Christophe
  %I Springer
  %P 531-534
  %T Namelings - Discover Given Name Relatedness Based on Data from the Social Web.
  %U https://www.kde.cs.uni-kassel.de/pub/pdf/mitzlaff2012namelings.pdf
  %V 7710
  %@ 978-3-642-35385-7
  

2011

• 1.

  Atzmueller, M., Mitzlaff, F.: Efficient Descriptive Community Mining. In: Proc. 24th Intl. FLAIRS Conference. AAAI Press (2011).

  BibTeXEndNote

  @inproceedings{atzmueller2011efficient,
  author = {Atzmueller, Martin and Mitzlaff, Folke},
  booktitle = {Proc. 24th Intl. FLAIRS Conference},
  keywords = {itegpub},
  publisher = {AAAI Press},
  title = {Efficient Descriptive Community Mining},
  year = 2011
  }
  

  %0 Conference Paper
  %1 atzmueller2011efficient
  %A Atzmueller, Martin
  %A Mitzlaff, Folke
  %B Proc. 24th Intl. FLAIRS Conference
  %D 2011
  %I AAAI Press
  %T Efficient Descriptive Community Mining
  

• 1.

  Mitzlaff, F., Atzmueller, M., Benz, D., Hotho, A., Stumme, G.: {Community Assessment using Evidence Networks}. In: Analysis of Social Media and Ubiquitous Data (2011).

  BibTeXEndNote

  @inproceedings{mitzlaff2011community,
  author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Analysis of Social Media and Ubiquitous Data},
  keywords = {community},
  series = {LNAI},
  title = {{Community Assessment using Evidence Networks}},
  volume = 6904,
  year = 2011
  }
  

  %0 Conference Paper
  %1 mitzlaff2011community
  %A Mitzlaff, Folke
  %A Atzmueller, Martin
  %A Benz, Dominik
  %A Hotho, Andreas
  %A Stumme, Gerd
  %B Analysis of Social Media and Ubiquitous Data
  %D 2011
  %T {Community Assessment using Evidence Networks}
  %V 6904
  

• 1.

  Atzmueller, M., Doerfel, S., Hotho, A., Mitzlaff, F., Stumme, G.: Face-to-Face Contacts during LWA 2010 - Communities, Roles, and Key Players. In: Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation (2011).

  BibTeXEndNote

  @inproceedings{atzmueller2011facetoface,
  author = {Atzmueller, Martin and Doerfel, Stephan and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd},
  booktitle = {Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation},
  keywords = {analysis},
  title = {Face-to-Face Contacts during LWA 2010 - Communities, Roles, and Key Players},
  year = 2011
  }
  

  %0 Conference Paper
  %1 atzmueller2011facetoface
  %A Atzmueller, Martin
  %A Doerfel, Stephan
  %A Hotho, Andreas
  %A Mitzlaff, Folke
  %A Stumme, Gerd
  %B Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation
  %D 2011
  %T Face-to-Face Contacts during LWA 2010 - Communities, Roles, and Key Players
  

• 1.

  Mitzlaff, F., Atzmueller, M., Benz, D., Hotho, A., Stumme, G.: Community Assessment Using Evidence Networks. In: Atzmueller, M., Hotho, A., Strohmaier, M., and Chin, A. (eds.) Analysis of Social Media and Ubiquitous Data. pp. 79–98. Springer Berlin Heidelberg (2011). https://doi.org/10.1007/978-3-642-23599-3_5.

  URLBibTeXEndNote

  Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups.

  @incollection{noKey,
  abstract = {Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups.},
  author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Analysis of Social Media and Ubiquitous Data},
  editor = {Atzmueller, Martin and Hotho, Andreas and Strohmaier, Markus and Chin, Alvin},
  keywords = {COMMUNE},
  pages = {79-98},
  publisher = {Springer Berlin Heidelberg},
  series = {Lecture Notes in Computer Science},
  title = {Community Assessment Using Evidence Networks},
  volume = 6904,
  year = 2011
  }
  

  %0 Book Section
  %1 noKey
  %A Mitzlaff, Folke
  %A Atzmueller, Martin
  %A Benz, Dominik
  %A Hotho, Andreas
  %A Stumme, Gerd
  %B Analysis of Social Media and Ubiquitous Data
  %D 2011
  %E Atzmueller, Martin
  %E Hotho, Andreas
  %E Strohmaier, Markus
  %E Chin, Alvin
  %I Springer Berlin Heidelberg
  %P 79-98
  %R 10.1007/978-3-642-23599-3_5
  %T Community Assessment Using Evidence Networks
  %U http://dx.doi.org/10.1007/978-3-642-23599-3_5
  %V 6904
  %X Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups.
  %@ 978-3-642-23598-6
  

• 1.

  Atzmüller, M., Benz, D., Doerfel, S., Hotho, A., Jäschke, R., Macek, B.E., Mitzlaff, F., Scholz, C., Stumme, G.: Enhancing Social Interactions at Conferences. it - Information Technology. 53, 101–107 (2011).

  URLBibTeXEndNote

  @article{atzmueller2011enhancing,
  author = {Atzmüller, 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},
  journal = {it - Information Technology},
  keywords = {COMMUNE},
  number = 3,
  pages = {101-107},
  title = {Enhancing Social Interactions at Conferences.},
  volume = 53,
  year = 2011
  }
  

  %0 Journal Article
  %1 atzmueller2011enhancing
  %A Atzmüller, Martin
  %A Benz, Dominik
  %A Doerfel, Stephan
  %A Hotho, Andreas
  %A Jäschke, Robert
  %A Macek, Bjoern Elmar
  %A Mitzlaff, Folke
  %A Scholz, Christoph
  %A Stumme, Gerd
  %D 2011
  %J it - Information Technology
  %N 3
  %P 101-107
  %T Enhancing Social Interactions at Conferences.
  %U http://dblp.uni-trier.de/db/journals/it/it53.html#AtzmullerBDHJMMSS11
  %V 53
  

• 1.

  Mitzlaff, F., Atzmueller, M., Benz, D., Hotho, A., Stumme, G.: Structure and Consistency: Assessment of Social Bookmarking Communities. In: Proceedings from Sunbelt XXXI. , Trade Winds Beach Resort (2011).

  BibTeXEndNote

  @inproceedings{mitzlaff2011social,
  address = {Trade Winds Beach Resort},
  author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Proceedings from Sunbelt XXXI},
  keywords = 2011,
  title = {Structure and Consistency: Assessment of Social Bookmarking Communities},
  year = 2011
  }
  

  %0 Conference Paper
  %1 mitzlaff2011social
  %A Mitzlaff, Folke
  %A Atzmueller, Martin
  %A Benz, Dominik
  %A Hotho, Andreas
  %A Stumme, Gerd
  %B Proceedings from Sunbelt XXXI
  %C Trade Winds Beach Resort
  %D 2011
  %T Structure and Consistency: Assessment of Social Bookmarking Communities
  

2010

• 1.

  Hotho, A., Benz, D., Eisterlehner, F., J{ä}schke, R., Krause, B., Schmitz, C., Stumme, G.: {Publikationsmanagement mit BibSonomy -- ein Social-Bookmarking-System f{ü}r Wissenschaftler}. HMD -- Praxis der Wirtschaftsinformatik. Heft 271, 47–58 (2010).

  BibTeXEndNote

  Kooperative Verschlagwortungs- bzw. Social-Bookmarking-Systeme wie Delicious, Mister Wong oder auch unser eigenes System BibSonomy erfreuen sich immer gr{ö}ßerer Beliebtheit und bilden einen zentralen Bestandteil des heutigen Web 2.0. In solchen Systemen erstellen Nutzer leichtgewichtige Begriffssysteme, sogenannte Folksonomies, die die Nutzerdaten strukturieren. Die einfache Bedienbarkeit, die Allgegenw{ä}rtigkeit, die st{ä}ndige Verf{ü}gbarkeit, aber auch die M{ö}glichkeit, Gleichgesinnte spontan in solchen Systemen zu entdecken oder sie schlicht als Informationsquelle zu nutzen, sind Gr{ü}nde f{ü}r ihren gegenw{ä}rtigen Erfolg. Der Artikel f{ü}hrt den Begriff Social Bookmarking ein und diskutiert zentrale Elemente (wie Browsing und Suche) am Beispiel von BibSonomy anhand typischer Arbeitsabl{ä}ufe eines Wissenschaftlers. Wir beschreiben die Architektur von BibSonomy sowie Wege der Integration und Vernetzung von BibSonomy mit Content-Management-Systemen und Webauftritten. Der Artikel schließt mit Querbez{ü}gen zu aktuellen Forschungsfragen im Bereich Social Bookmarking.

  @article{HothoBenzEtAl10hmd,
  abstract = {Kooperative Verschlagwortungs- bzw. Social-Bookmarking-Systeme wie Delicious, Mister Wong oder auch unser eigenes System BibSonomy erfreuen sich immer gr{ö}ßerer Beliebtheit und bilden einen zentralen Bestandteil des heutigen Web 2.0. In solchen Systemen erstellen Nutzer leichtgewichtige Begriffssysteme, sogenannte Folksonomies, die die Nutzerdaten strukturieren. Die einfache Bedienbarkeit, die Allgegenw{ä}rtigkeit, die st{ä}ndige Verf{ü}gbarkeit, aber auch die M{ö}glichkeit, Gleichgesinnte spontan in solchen Systemen zu entdecken oder sie schlicht als Informationsquelle zu nutzen, sind Gr{ü}nde f{ü}r ihren gegenw{ä}rtigen Erfolg. Der Artikel f{ü}hrt den Begriff Social Bookmarking ein und diskutiert zentrale Elemente (wie Browsing und Suche) am Beispiel von BibSonomy anhand typischer Arbeitsabl{ä}ufe eines Wissenschaftlers. Wir beschreiben die Architektur von BibSonomy sowie Wege der Integration und Vernetzung von BibSonomy mit Content-Management-Systemen und Webauftritten. Der Artikel schließt mit Querbez{ü}gen zu aktuellen Forschungsfragen im Bereich Social Bookmarking.},
  author = {Hotho, Andreas and Benz, Dominik and Eisterlehner, Folke and J{ä}schke, Robert and Krause, Beate and Schmitz, Christoph and Stumme, Gerd},
  journal = {HMD -- Praxis der Wirtschaftsinformatik},
  keywords = 2010,
  month = {02},
  pages = {47-58},
  title = {{Publikationsmanagement mit BibSonomy -- ein Social-Bookmarking-System f{ü}r Wissenschaftler}},
  volume = {Heft 271},
  year = 2010
  }
  

  %0 Journal Article
  %1 HothoBenzEtAl10hmd
  %A Hotho, Andreas
  %A Benz, Dominik
  %A Eisterlehner, Folke
  %A J{ä}schke, Robert
  %A Krause, Beate
  %A Schmitz, Christoph
  %A Stumme, Gerd
  %D 2010
  %J HMD -- Praxis der Wirtschaftsinformatik
  %P 47-58
  %T {Publikationsmanagement mit BibSonomy -- ein Social-Bookmarking-System f{ü}r Wissenschaftler}
  %V Heft 271
  %X Kooperative Verschlagwortungs- bzw. Social-Bookmarking-Systeme wie Delicious, Mister Wong oder auch unser eigenes System BibSonomy erfreuen sich immer gr{ö}ßerer Beliebtheit und bilden einen zentralen Bestandteil des heutigen Web 2.0. In solchen Systemen erstellen Nutzer leichtgewichtige Begriffssysteme, sogenannte Folksonomies, die die Nutzerdaten strukturieren. Die einfache Bedienbarkeit, die Allgegenw{ä}rtigkeit, die st{ä}ndige Verf{ü}gbarkeit, aber auch die M{ö}glichkeit, Gleichgesinnte spontan in solchen Systemen zu entdecken oder sie schlicht als Informationsquelle zu nutzen, sind Gr{ü}nde f{ü}r ihren gegenw{ä}rtigen Erfolg. Der Artikel f{ü}hrt den Begriff Social Bookmarking ein und diskutiert zentrale Elemente (wie Browsing und Suche) am Beispiel von BibSonomy anhand typischer Arbeitsabl{ä}ufe eines Wissenschaftlers. Wir beschreiben die Architektur von BibSonomy sowie Wege der Integration und Vernetzung von BibSonomy mit Content-Management-Systemen und Webauftritten. Der Artikel schließt mit Querbez{ü}gen zu aktuellen Forschungsfragen im Bereich Social Bookmarking.
  

• 1.

  Benz, D., Hotho, A., Jäschke, R., Krause, B., Mitzlaff, F., Schmitz, C., Stumme, G.: The social bookmark and publication management system bibsonomy. The VLDB Journal. 19, 849–875 (2010). https://doi.org/10.1007/s00778-010-0208-4.

  URLBibTeXEndNote

  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.

  @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.},
  address = {Berlin / Heidelberg},
  author = {Benz, Dominik and Hotho, Andreas and Jäschke, Robert and Krause, Beate and Mitzlaff, Folke and Schmitz, Christoph and Stumme, Gerd},
  journal = {The VLDB Journal},
  keywords = {bibsonomy},
  pages = {849-875},
  publisher = {Springer},
  title = {The social bookmark and publication management system bibsonomy},
  volume = 19,
  year = 2010
  }
  

  %0 Journal Article
  %1 benz2010social
  %A Benz, Dominik
  %A Hotho, Andreas
  %A Jäschke, Robert
  %A Krause, Beate
  %A Mitzlaff, Folke
  %A Schmitz, Christoph
  %A Stumme, Gerd
  %C Berlin / Heidelberg
  %D 2010
  %I Springer
  %J The VLDB Journal
  %P 849-875
  %R 10.1007/s00778-010-0208-4
  %T The social bookmark and publication management system bibsonomy
  %U http://dx.doi.org/10.1007/s00778-010-0208-4
  %V 19
  %X 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.
  

• 1.

  Mitzlaff, F., Benz, D., Stumme, G., Hotho, A.: Visit me, click me, be my friend: an analysis of evidence networks of user relationships in BibSonomy. In: Proceedings of the 21st ACM conference on Hypertext and hypermedia. pp. 265–270. ACM, New York, NY, USA (2010).

  BibTeXEndNote

  @inproceedings{mitzlaff2010visit,
  address = {New York, NY, USA},
  author = {Mitzlaff, Folke and Benz, Dominik and Stumme, Gerd and Hotho, Andreas},
  booktitle = {Proceedings of the 21st ACM conference on Hypertext and hypermedia},
  keywords = {itegpub},
  pages = {265--270},
  publisher = {ACM},
  series = {HT '10},
  title = {Visit me, click me, be my friend: an analysis of evidence networks of user relationships in BibSonomy},
  year = 2010
  }
  

  %0 Conference Paper
  %1 mitzlaff2010visit
  %A Mitzlaff, Folke
  %A Benz, Dominik
  %A Stumme, Gerd
  %A Hotho, Andreas
  %B Proceedings of the 21st ACM conference on Hypertext and hypermedia
  %C New York, NY, USA
  %D 2010
  %I ACM
  %P 265--270
  %T Visit me, click me, be my friend: an analysis of evidence networks of user relationships in BibSonomy
  

• 1.

  Mitzlaff, F., Atzmüller, M., Benz, D., Hotho, A., Stumme, G.: Community Assessment using Evidence Networks. In: Proceedings of the Workshop on Mining Ubiquitous and Social Environments (MUSE2010). , Barcelona, Spain (2010).

  URLBibTeXEndNote

  Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups. This paper proposes evidence networks using implicit information for the evaluation of communities. The presented evaluation approach is based on the idea of reconstructing existing social structures for the assessment and evaluation of a given clustering. We analyze and compare the presented evidence networks using user data from the real-world social bookmarking application BibSonomy. The results indicate that the evidence networks reflect the relative rating of the explicit ones very well.

  @inproceedings{mitzlaff2010community,
  abstract = {Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups. This paper proposes evidence networks using implicit information for the evaluation of communities. The presented evaluation approach is based on the idea of reconstructing existing social structures for the assessment and evaluation of a given clustering. We analyze and compare the presented evidence networks using user data from the real-world social bookmarking application BibSonomy. The results indicate that the evidence networks reflect the relative rating of the explicit ones very well.},
  address = {Barcelona, Spain},
  author = {Mitzlaff, Folke and Atzmüller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Proceedings of the Workshop on Mining Ubiquitous and Social Environments (MUSE2010)},
  keywords = {COMMUNE},
  title = {Community Assessment using Evidence Networks},
  year = 2010
  }
  

  %0 Conference Paper
  %1 mitzlaff2010community
  %A Mitzlaff, Folke
  %A Atzmüller, Martin
  %A Benz, Dominik
  %A Hotho, Andreas
  %A Stumme, Gerd
  %B Proceedings of the Workshop on Mining Ubiquitous and Social Environments (MUSE2010)
  %C Barcelona, Spain
  %D 2010
  %T Community Assessment using Evidence Networks
  %U https://www.kde.cs.uni-kassel.de/ws/muse2010
  %X Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups. This paper proposes evidence networks using implicit information for the evaluation of communities. The presented evaluation approach is based on the idea of reconstructing existing social structures for the assessment and evaluation of a given clustering. We analyze and compare the presented evidence networks using user data from the real-world social bookmarking application BibSonomy. The results indicate that the evidence networks reflect the relative rating of the explicit ones very well.
  

• 1.

  Atzmueller, M., Mitzlaff, F.: {Towards Mining Descriptive Community Patterns}. In: Workshop on Mining Patterns and Subgroups. Lorentz Center, Leiden, The Netherlands. Awarded with the Best Discovery Award (2010).

  BibTeXEndNote

  @inproceedings{atzmueller2010towards,
  author = {Atzmueller, Martin and Mitzlaff, Folke},
  booktitle = {Workshop on Mining Patterns and Subgroups},
  keywords = {itegpub},
  publisher = {Lorentz Center, Leiden, The Netherlands. Awarded with the Best Discovery Award},
  title = {{Towards Mining Descriptive Community Patterns}},
  year = 2010
  }
  

  %0 Conference Paper
  %1 atzmueller2010towards
  %A Atzmueller, Martin
  %A Mitzlaff, Folke
  %B Workshop on Mining Patterns and Subgroups
  %D 2010
  %I Lorentz Center, Leiden, The Netherlands. Awarded with the Best Discovery Award
  %T {Towards Mining Descriptive Community Patterns}
  

2009

• 1.

  Jäschke, R., Eisterlehner, F., Hotho, A., Stumme, G.: Testing and Evaluating Tag Recommenders in a Live System. In: Benz, D. and Janssen, F. (eds.) Workshop on Knowledge Discovery, Data Mining, and Machine Learning. pp. 44–51 (2009).

  URLBibTeXEndNote

  The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on evaluation anddevelopment of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance.In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible,open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents an evaluation of two exemplarily deployed recommendation methods, demonstratingthe power of the framework.

  @inproceedings{Jaeschke2009,
  abstract = {The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on evaluation anddevelopment of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance.In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible,open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents an evaluation of two exemplarily deployed recommendation methods, demonstratingthe power of the framework.},
  author = {Jäschke, Robert and Eisterlehner, Folke and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Workshop on Knowledge Discovery, Data Mining, and Machine Learning},
  editor = {Benz, Dominik and Janssen, Frederik},
  keywords = {bibsonomy},
  month = {09},
  pages = {44 --51},
  title = {Testing and Evaluating Tag Recommenders in a Live System},
  year = 2009
  }
  

  %0 Conference Paper
  %1 Jaeschke2009
  %A Jäschke, Robert
  %A Eisterlehner, Folke
  %A Hotho, Andreas
  %A Stumme, Gerd
  %B Workshop on Knowledge Discovery, Data Mining, and Machine Learning
  %D 2009
  %E Benz, Dominik
  %E Janssen, Frederik
  %P 44 --51
  %T Testing and Evaluating Tag Recommenders in a Live System
  %U http://lwa09.informatik.tu-darmstadt.de/pub/KDML/WebHome/kdml09_R.Jaeschke_et_al.pdf
  %X The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on evaluation anddevelopment of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance.In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible,open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents an evaluation of two exemplarily deployed recommendation methods, demonstratingthe power of the framework.
  

• 1.

  Eisterlehner, F., Hotho, A., Jäschke, R. eds.: ECML PKDD Discovery Challenge 2009 (DC09). (2009).

  URLBibTeXEndNote

  @proceedings{eisterlehner2009ecmlpkdd,
  editor = {Eisterlehner, Folke and Hotho, Andreas and Jäschke, Robert},
  keywords = {dc09},
  month = {09},
  series = {CEUR-WS.org},
  title = {ECML PKDD Discovery Challenge 2009 (DC09)},
  volume = 497,
  year = 2009
  }
  

  %0 Conference Proceedings
  %1 eisterlehner2009ecmlpkdd
  %B CEUR-WS.org
  %D 2009
  %E Eisterlehner, Folke
  %E Hotho, Andreas
  %E Jäschke, Robert
  %T ECML PKDD Discovery Challenge 2009 (DC09)
  %U http://ceur-ws.org/Vol-497
  %V 497
  

• 1.

  Benz, D., Eisterlehner, F., Hotho, A., Jäschke, R., Krause, B., Stumme, G.: Managing publications and bookmarks with BibSonomy. In: Cattuto, C., Ruffo, G., and Menczer, F. (eds.) HT ’09: Proceedings of the 20th ACM Conference on Hypertext and Hypermedia. pp. 323–324. ACM, New York, NY, USA (2009). https://doi.org/10.1145/1557914.1557969.

  URLBibTeXEndNote

  In this demo we present BibSonomy, a social bookmark and publication sharing system.

  @inproceedings{benz2009managing,
  abstract = {In this demo we present BibSonomy, a social bookmark and publication sharing system.},
  address = {New York, NY, USA},
  author = {Benz, Dominik and Eisterlehner, Folke and Hotho, Andreas and Jäschke, Robert and Krause, Beate and Stumme, Gerd},
  booktitle = {HT '09: Proceedings of the 20th ACM Conference on Hypertext and Hypermedia},
  editor = {Cattuto, Ciro and Ruffo, Giancarlo and Menczer, Filippo},
  keywords = {bibsonomy},
  month = {06},
  pages = {323--324},
  publisher = {ACM},
  title = {Managing publications and bookmarks with BibSonomy},
  year = 2009
  }
  

  %0 Conference Paper
  %1 benz2009managing
  %A Benz, Dominik
  %A Eisterlehner, Folke
  %A Hotho, Andreas
  %A Jäschke, Robert
  %A Krause, Beate
  %A Stumme, Gerd
  %B HT '09: Proceedings of the 20th ACM Conference on Hypertext and Hypermedia
  %C New York, NY, USA
  %D 2009
  %E Cattuto, Ciro
  %E Ruffo, Giancarlo
  %E Menczer, Filippo
  %I ACM
  %P 323--324
  %R 10.1145/1557914.1557969
  %T Managing publications and bookmarks with BibSonomy
  %U http://portal.acm.org/citation.cfm?doid=1557914.1557969#
  %X In this demo we present BibSonomy, a social bookmark and publication sharing system.
  %@ 978-1-60558-486-7
  

• 1.

  Jäschke, R., Eisterlehner, F., Hotho, A., Stumme, G.: Testing and Evaluating Tag Recommenders in a Live System. In: RecSys ’09: Proceedings of the third ACM Conference on Recommender Systems. pp. 369–372. ACM, New York, NY, USA (2009). https://doi.org/10.1145/1639714.1639790.

  URLBibTeXEndNote

  The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on the evaluation and development of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance. In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible, open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents a �rst evaluation of two exemplarily deployed recommendation methods.

  @inproceedings{jaeschke2009testing,
  abstract = {The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on the evaluation and development of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance. In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible, open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents a �rst evaluation of two exemplarily deployed recommendation methods.},
  address = {New York, NY, USA},
  author = {Jäschke, Robert and Eisterlehner, Folke and Hotho, Andreas and Stumme, Gerd},
  booktitle = {RecSys '09: Proceedings of the third ACM Conference on Recommender Systems},
  keywords = {bibsonomy},
  pages = {369--372},
  publisher = {ACM},
  title = {Testing and Evaluating Tag Recommenders in a Live System},
  year = 2009
  }
  

  %0 Conference Paper
  %1 jaeschke2009testing
  %A Jäschke, Robert
  %A Eisterlehner, Folke
  %A Hotho, Andreas
  %A Stumme, Gerd
  %B RecSys '09: Proceedings of the third ACM Conference on Recommender Systems
  %C New York, NY, USA
  %D 2009
  %I ACM
  %P 369--372
  %R 10.1145/1639714.1639790
  %T Testing and Evaluating Tag Recommenders in a Live System
  %U https://www.kde.cs.uni-kassel.de/pub/pdf/jaeschke2009testing.pdf
  %X The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on the evaluation and development of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance. In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible, open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents a �rst evaluation of two exemplarily deployed recommendation methods.
  %@ 978-1-60558-435-5