@inproceedings{Landia:2012:EFC:2365934.2365936, 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.}, acmid = {2365936}, address = {New York, NY, USA}, author = {Landia, Nikolas and Anand, Sarabjot Singh and Hotho, Andreas and J\"{a}schke, 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 = {2ce2874d37fd3b90c9f6a46a7a08e94b}, intrahash = {a97bf903435d6fc4fc61e2bb7e3913b9}, 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{hotho2006information, address = {Heidelberg}, author = {Hotho, Andreas and J?schke, Robert and Schmitz, Christoph and Stumme, Gerd}, booktitle = {The Semantic Web: Research and Applications}, editor = {Sure, York and Domingue, John}, interhash = {aa0a40dd836bfde8397409adfdc4a3f2}, intrahash = {b1e4dabc5b558aeea1b839a7f123eef1}, month = {June}, pages = {411-426}, publisher = {Springer}, series = {LNAI}, title = {Information Retrieval in Folksonomies: Search and Ranking}, volume = 4011, year = 2006 } @inbook{schmitz2006kollaboratives, abstract = {Wissensmanagement in zentralisierten Wissensbasen erfordert einen hohen Aufwand f�r Erstellung und Wartung, und es entspricht nicht immer den Anforderungen der Benutzer. Wir geben in diesem Kapitel einen �berblick �ber zwei aktuelle Ans�tze, die durch kollaboratives Wissensmanagement diese Probleme l�sen k�nnen. Im Peer-to-Peer-Wissensmanagement unterhalten Benutzer dezentrale Wissensbasen, die dann vernetzt werden k�nnen, um andere Benutzer eigene Inhalte nutzen zu lassen. Folksonomies versprechen, die Wissensakquisition so einfach wie m�glich zu gestalten und so viele Benutzer in den Aufbau und die Pflege einer gemeinsamen Wissensbasis einzubeziehen.}, author = {Schmitz, Christoph and Hotho, Andreas and J�schke, Robert and Stumme, Gerd}, editor = {Pellegrini, Tassilo and Blumauer, Andreas}, file = {schmitz2006kollaboratives.pdf:schmitz2006kollaboratives.pdf:PDF}, interhash = {a3102df5e75137fa4a95c718f470fd39}, intrahash = {923e175b1912828ede540759dde1700a}, lastdatemodified = {2007-04-27}, lastname = {Schmitz}, longnotes = {[[http://www.semantic-web.at/springer/abstracts/3d_Schmitz_KollabWM.pdf abstract (pdf)]]}, own = {own}, pages = {273-290}, pdf = {schmitz06-kollaboratives.pdf}, publisher = {Springer}, read = {any}, title = {Kollaboratives Wissensmanagement}, year = 2006 } @inproceedings{schmitz2006mining, abstract = {Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. These systems provide currently relatively few structure. We discuss in this paper, how association rule mining can be adopted to analyze and structure folksonomies, and how the results can be used for ontology learning and supporting emergent semantics. We demonstrate our approach on a large scale dataset stemming from an online system.}, address = {Heidelberg}, author = {Schmitz, Christoph and Hotho, Andreas and J�schke, Robert and Stumme, Gerd}, booktitle = {Data Science and Classification. Proceedings of the 10th IFCS Conf.}, editor = {Batagelj, V. and Bock, H.-H. and Ferligoj, A. and �iberna, A.}, file = {schmitz2006mining.pdf:schmitz2006mining.pdf:PDF}, interhash = {9f407e0b779aba5b3afca7fb906f579b}, intrahash = {91a9a847b72a77e8f7d7db4de52716e5}, lastdatemodified = {2006-12-07}, lastname = {Schmitz}, month = {July}, own = {notown}, pages = {261--270}, pdf = {schmitz06-mining.pdf}, publisher = {Springer}, read = {notread}, series = {Studies in Classification, Data Analysis, and Knowledge Organization}, title = {Mining Association Rules in Folksonomies}, year = 2006 } @inproceedings{jaeschke2007analysis, abstract = {BibSonomy is a web-based social resource sharing system which allows users to organise and share bookmarks and publications in a collaborative manner. In this paper we present the system, followed by a description of the insights in the structure of its bibliographic data that we gained by applying techniques we developed in the area of Formal Concept Analysis.}, address = {Berlin, Heidelberg}, at = {2007-08-23 20:10:55}, author = {J\"{a}schke, Robert and Hotho, Andreas and Schmitz, Christoph and Stumme, Gerd}, booktitle = {Proceedings of the 15th International Conference on Conceptual Structures (ICCS 2007)}, citeulike-article-id = {1586648}, editor = {Priss, U. and Polovina, S. and Hill, R.}, id = {1586648}, interhash = {c5d14199c65245bcec9ece9d62373312}, intrahash = {a0cd4cfefeb320b5b0e837069cb94265}, month = {July}, pages = {283--295}, posted-at = {2007-08-23 20:10:55}, priority = {4}, publisher = {Springer-Verlag}, series = {Lecture Notes in Artificial Intelligence}, title = {Analysis of the Publication Sharing Behaviour in {BibSonomy}}, volume = 4604, year = 2007 } @incollection{citeulike:1377860, abstract = {Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. These systems provide currently relatively few structure. We discuss in this paper, how association rule mining can be adopted to analyze and structure folksonomies, and how the results can be used for ontology learning and supporting emergent semantics. We demonstrate our approach on a large scale dataset stemming from an online system.}, author = {Schmitz, Christoph and Hotho, Andreas and J\"{a}schke, Robert and Stumme, Gerd}, citeulike-article-id = {1377860}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/3-540-34416-0\_28}, doi = {10.1007/3-540-34416-0\_28}, interhash = {b4a63aa5632ff093c2d345005fa16a17}, intrahash = {06ea55e8751a06c3b44a92543dd6e85a}, journal = {Data Science and Classification}, pages = {261--270}, posted-at = {2008-04-27 16:27:04}, priority = {5}, title = {Mining Association Rules in Folksonomies}, url = {http://dx.doi.org/10.1007/3-540-34416-0\_28}, year = 2006 } @inproceedings{hotho2006information, address = {Heidelberg}, author = {Hotho, Andreas and J�schke, Robert and Schmitz, Christoph and Stumme, Gerd}, booktitle = {The Semantic Web: Research and Applications}, editor = {Sure, York and Domingue, John}, interhash = {aa0a40dd836bfde8397409adfdc4a3f2}, intrahash = {b1e4dabc5b558aeea1b839a7f123eef1}, month = {June}, pages = {411-426}, publisher = {Springer}, series = {LNAI}, title = {Information Retrieval in Folksonomies: Search and Ranking}, volume = 4011, year = 2006 }