@inproceedings{wetzker2010translating,
  abstract = {Collaborative tagging services (folksonomies) have been among the stars of the Web 2.0 era. They allow their users to label diverse resources with freely chosen keywords (tags). Our studies of two real-world folksonomies unveil that individual users develop highly personalized vocabularies of tags. While these meet individual needs and preferences, the considerable differences between personal tag vocabularies (personomies) impede services such as social search or customized tag recommendation. In this paper, we introduce a novel user-centric tag model that allows us to derive mappings between personal tag vocabularies and the corresponding folksonomies. Using these mappings, we can infer the meaning of user-assigned tags and can predict choices of tags a user may want to assign to new items. Furthermore, our translational approach helps in reducing common problems related to tag ambiguity, synonymous tags, or multilingualism. We evaluate the applicability of our method in tag recommendation and tag-based social search. Extensive experiments show that our translational model improves the prediction accuracy in both scenarios.},
  acmid = {1718497},
  address = {New York, NY, USA},
  author = {Wetzker, Robert and Zimmermann, Carsten and Bauckhage, Christian and Albayrak, Sahin},
  booktitle = {Proceedings of the third ACM international conference on Web search and data mining},
  doi = {10.1145/1718487.1718497},
  interhash = {12e89c88182a393dae8d63287f65540d},
  intrahash = {224e7bdc753e1823fc17828f2c760b6e},
  isbn = {978-1-60558-889-6},
  location = {New York, New York, USA},
  numpages = {10},
  pages = {71--80},
  publisher = {ACM},
  series = {WSDM '10},
  title = {I tag, you tag: translating tags for advanced user models},
  url = {http://doi.acm.org/10.1145/1718487.1718497},
  year = 2010
}

@article{10.1109/CSE.2009.75,
  address = {Los Alamitos, CA, USA},
  author = {Zhou, Tom Chao and Ma, Hao and King, Irwin and Lyu, Michael R.},
  doi = {10.1109/CSE.2009.75},
  interhash = {010aefb7b22a666044909f4cea151963},
  intrahash = {2b9dd91a3162d821abbe620942772464},
  isbn = {978-0-7695-3823-5},
  journal = {Computational Science and Engineering, IEEE International Conference on},
  pages = {194-199},
  publisher = {IEEE Computer Society},
  title = {TagRec: Leveraging Tagging Wisdom for Recommendation},
  url = {http://www.computer.org/portal/web/csdl/doi/10.1109/CSE.2009.75},
  volume = 4,
  year = 2009
}

@inproceedings{1661779,
  abstract = {A folksonomy refers to a collection of user-defined tags with which users describe contents published on the Web. With the flourish of Web 2.0, folksonomies have become an important mean to develop the Semantic Web. Because tags in folksonomies are authored freely, there is a need to understand the structure and semantics of these tags in various applications. In this paper, we propose a learning approach to create an ontology that captures the hierarchical semantic structure of folksonomies. Our experimental results on two different genres of real world data sets show that our method can effectively learn the ontology structure from the folksonomies.},
  address = {San Francisco, CA, USA},
  author = {Tang, Jie and fung Leung, Ho and Luo, Qiong and Chen, Dewei and Gong, Jibin},
  booktitle = {IJCAI'09: Proceedings of the 21st international jont conference on Artifical intelligence},
  interhash = {17f95a6ba585888cf45443926d8b7e98},
  intrahash = {7b335f08a288a79eb70eff89f1ec7630},
  location = {Pasadena, California, USA},
  pages = {2089--2094},
  publisher = {Morgan Kaufmann Publishers Inc.},
  title = {Towards ontology learning from folksonomies},
  url = {http://ijcai.org/papers09/Papers/IJCAI09-344.pdf},
  year = 2009
}

@article{charniak97statistical,
  author = {Charniak, Eugene},
  interhash = {9f0c334b655cfa509f9862a0569cd375},
  intrahash = {1d02e8f9d663f5cd8203ec6685a958ed},
  journal = {AI Magazine},
  number = 4,
  pages = {33-44},
  title = {Statistical Techniques for Natural Language Parsing},
  url = {http://citeseer.ist.psu.edu/article/charniak97statistical.html},
  volume = 18,
  year = 1997
}

@article{cattuto-2007-104,
  abstract = { Collaborative tagging has been quickly gaining ground because of its ability to recruit the activity of web users into effectively organizing and sharing vast amounts of information. Here we collect data from a popular system and investigate the statistical properties of tag co-occurrence. We introduce a stochastic model of user behavior embodying two main aspects of collaborative tagging: (i) a frequency-bias mechanism related to the idea that users are exposed to each other's tagging activity; (ii) a notion of memory - or aging of resources - in the form of a heavy-tailed access to the past state of the system. Remarkably, our simple modeling is able to account quantitatively for the observed experimental features, with a surprisingly high accuracy. This points in the direction of a universal behavior of users, who - despite the complexity of their own cognitive processes and the uncoordinated and selfish nature of their tagging activity - appear to follow simple activity patterns.},
  author = {Cattuto, Ciro and Loreto, Vittorio and Pietronero, Luciano},
  interhash = {5859f32f4999e1fbf6f4c59cc89fa2c1},
  intrahash = {c958b2f97a0557abdb3778faa34802bb},
  journal = {PNAS},
  pages = 1461,
  title = {Collaborative Tagging and Semiotic Dynamics},
  url = {doi:10.1073/pnas.0610487104},
  volume = 104,
  year = 2007
}