@book{doerfel2013informationelle,
  author = {Doerfel, Stephan and Hotho, Andreas and Kartal-Aydemir, Aliye and Roßnagel, Alexander and Stumme, Gerd},
  interhash = {f72d297ba42797ca66baba052c846b7a},
  intrahash = {2bb934c0ff3652843fd0aff97d8d7324},
  isbn = {9783642380556 3642380557},
  publisher = {Vieweg + Teubner Verlag},
  refid = {857973438},
  title = {Informationelle Selbstbestimmung Im Web 2.0 Chancen Und Risiken Sozialer Verschlagwortungssysteme},
  url = {http://www.worldcat.org/search?qt=worldcat_org_all&q=9783642380556},
  year = 2013
}

@inproceedings{atzmueller2012ubicon,
  author = {Atzmueller, Martin and Becker, Martin and Doerfel, Stephan and Kibanov, Mark and Hotho, Andreas and Macek, Bj\"orn-Elmar and Mitzlaff, Folke and Mueller, Juergen and Scholz, Christoph and Stumme, Gerd},
  booktitle = {Proc. 4th IEEE Intl. Conf. on Cyber, Physical and Social Computing (CPSCom 2012)},
  interhash = {a2695fd9fe6e76b252edbd42d72b34ad},
  intrahash = {5ff39a371dadedefd3abba84b26fd0e7},
  title = {Ubicon: Observing Social and Physical Activities},
  year = 2012
}

@incollection{atzmueller2012facetoface,
  address = {Heidelberg, Germany},
  alteditor = {Editor},
  author = {Atzmueller, Martin and Doerfel, Stephan and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd},
  booktitle = {{Modeling and Mining Ubiquitous Social Media}},
  interhash = {4f1f4b515b01cc448a91b3e368deabad},
  intrahash = {d81d6f6ccdf3ff6572898d39c90e6354},
  publisher = {Springer Verlag},
  series = {LNAI},
  title = {Face-to-Face Contacts at a Conference: Dynamics of Communities and Roles},
  volume = 7472,
  year = 2012
}

@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.</p> <p>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.},
  acmid = {2365936},
  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},
  doi = {10.1145/2365934.2365936},
  interhash = {7400e35f8d412d15722fe3399aba14a3},
  intrahash = {200a05b24a08dd33e377838ae5bdcf71},
  isbn = {978-1-4503-1638-5},
  location = {Dublin, Ireland},
  numpages = {8},
  pages = {1--8},
  publisher = {ACM},
  series = {RSWeb '12},
  title = {Extending FolkRank with content data},
  url = {http://doi.acm.org/10.1145/2365934.2365936},
  year = 2012
}

@inproceedings{doerfel2012leveraging,
  abstract = {The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.},
  acmid = {2365937},
  address = {New York, NY, USA},
  author = {Doerfel, Stephan and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web},
  doi = {10.1145/2365934.2365937},
  interhash = {beb2c81daf975eeed6e01e1b412196b1},
  intrahash = {d3e6fa8023b173228a959914affc8d73},
  isbn = {978-1-4503-1638-5},
  location = {Dublin, Ireland},
  numpages = {8},
  pages = {9--16},
  publisher = {ACM},
  series = {RSWeb '12},
  title = {Leveraging Publication Metadata and Social Data into FolkRank for Scientific Publication Recommendation},
  url = {http://doi.acm.org/10.1145/2365934.2365937},
  year = 2012
}