@misc{Bollen2009,
  abstract = {  Most tagging systems support the user in the tag selection process by
providing tag suggestions, or recommendations, based on a popularity
measurement of tags other users provided when tagging the same resource. In
this paper we investigate the influence of tag suggestions on the emergence of
power law distributions as a result of collaborative tag behavior. Although
previous research has already shown that power laws emerge in tagging systems,
the cause of why power law distributions emerge is not understood empirically.
The majority of theories and mathematical models of tagging found in the
literature assume that the emergence of power laws in tagging systems is mainly
driven by the imitation behavior of users when observing tag suggestions
provided by the user interface of the tagging system. This imitation behavior
leads to a feedback loop in which some tags are reinforced and get more popular
which is also known as the `rich get richer' or a preferential attachment
model. We present experimental results that show that the power law
distribution forms regardless of whether or not tag suggestions are presented
to the users. Furthermore, we show that the real effect of tag suggestions is
rather subtle; the resulting power law distribution is `compressed' if tag
suggestions are given to the user, resulting in a shorter long tail and a
`compressed' top of the power law distribution. The consequences of this
experiment show that tag suggestions by themselves do not account for the
formation of power law distributions in tagging systems.
},
  author = {Bollen, Dirk and Halpin, Harry},
  interhash = {280a97ee745f4e0409cf031a1b7ea247},
  intrahash = {07fe71c72f4fe79cb5a16f53048e0abe},
  note = {cite arxiv:0903.1788
},
  title = {The Role of Tag Suggestions in Folksonomies},
  url = {http://arxiv.org/abs/0903.1788},
  year = 2009
}

@inproceedings{schmitz2006mining,
  address = {Berlin/Heidelberg},
  author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
  booktitle = {Data Science and Classification (Proc. IFCS 2006 Conference)},
  doi = {10.1007/3-540-34416-0_28},
  editor = {Batagelj, V. and Bock, H.-H. and Ferligoj, A. and Žiberna, A.},
  interhash = {20650d852ca3b82523fcd8b63e7c12d7},
  intrahash = {c8dbb6371be8d67e3aa1928bd3dd0fed},
  isbn = {978-3-540-34415-5},
  month = {July},
  note = {Ljubljana},
  pages = {261-270},
  publisher = {Springer},
  series = {Studies in Classification, Data Analysis, and Knowledge Organization},
  title = {Mining Association Rules in Folksonomies},
  url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2006/schmitz2006asso_ifcs.pdf},
  vgwort = {18},
  year = 2006
}

@inproceedings{anti2008krause,
  address = {New York, NY, USA},
  author = {Krause, Beate and Schmitz, Christoph and Hotho, Andreas and Stumme, Gerd},
  booktitle = {AIRWeb '08: Proceedings of the 4th international workshop on Adversarial information retrieval on the web},
  doi = {http://doi.acm.org/10.1145/1451983.1451998},
  interhash = {a45d40ac7776551301ad9dde5b25357f},
  intrahash = {68effe5d4b9460f9388e7685310f74c2},
  isbn = {978-1-60558-159-0},
  location = {Beijing, China},
  pages = {61--68},
  publisher = {ACM},
  title = {The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems},
  url = {http://airweb.cse.lehigh.edu/2008/submissions/krause_2008_anti_social_tagger.pdf},
  year = 2008
}

@inproceedings{siersdorfer2009social,
  abstract = {The rapidly increasing popularity of Web 2.0 knowledge and content sharing systems and growing amount of shared data make discovering relevant content and finding contacts a difficult enterprize. Typically, folksonomies provide a rich set of structures and social relationships that can be mined for a variety of recommendation purposes. In this paper we propose a formal model to characterize users, items, and annotations in Web 2.0 environments. Our objective is to construct social recommender systems that predict the utility of items, users, or groups based on the multi-dimensional social environment of a given user. Based on this model we introduce recommendation mechanisms for content sharing frameworks. Our comprehensive evaluation shows the viability of our approach and emphasizes the key role of social meta knowledge for constructing effective recommendations in Web 2.0 applications.},
  address = {New York, NY, USA},
  author = {Siersdorfer, Stefan and Sizov, Sergej},
  booktitle = {HT '09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia},
  interhash = {9245d0a556113aa107ba8171f3897156},
  intrahash = {bbf0c98e0ab32612109e6688de81c432},
  month = {July},
  paperid = {fp091},
  publisher = {ACM},
  session = {Full Paper},
  title = {Social Recommender Systems for Web 2.0 Folksonomies},
  year = 2009
}

@inproceedings{1557100,
  abstract = {Tag recommendation is the task of predicting a personalized list of tags for a user given an item. This is important for many websites with tagging capabilities like last.fm or delicious. In this paper, we propose a method for tag recommendation based on tensor factorization (TF). In contrast to other TF methods like higher order singular value decomposition (HOSVD), our method RTF ('ranking with tensor factorization') directly optimizes the factorization model for the best personalized ranking. RTF handles missing values and learns from pairwise ranking constraints. Our optimization criterion for TF is motivated by a detailed analysis of the problem and of interpretation schemes for the observed data in tagging systems. In all, RTF directly optimizes for the actual problem using a correct interpretation of the data. We provide a gradient descent algorithm to solve our optimization problem. We also provide an improved learning and prediction method with runtime complexity analysis for RTF. The prediction runtime of RTF is independent of the number of observations and only depends on the factorization dimensions. Besides the theoretical analysis, we empirically show that our method outperforms other state-of-the-art tag recommendation methods like FolkRank, PageRank and HOSVD both in quality and prediction runtime.},
  address = {New York, NY, USA},
  author = {Rendle, Steffen and Marinho, Leandro Balby and Nanopoulos, Alexandros and Schmidt-Thieme, Lars},
  booktitle = {KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining},
  doi = {http://doi.acm.org/10.1145/1557019.1557100},
  interhash = {1cc85ca2ec82db2a3caf40fd1795a58a},
  intrahash = {1bd672ffb8d6ba5589bb0c7deca09412},
  isbn = {978-1-60558-495-9},
  location = {Paris, France},
  pages = {727--736},
  publisher = {ACM},
  title = {Learning optimal ranking with tensor factorization for tag recommendation},
  url = {http://portal.acm.org/citation.cfm?id=1557019.1557100&coll=ACM&dl=ACM&type=series&idx=SERIES939&part=series&WantType=Proceedings&title=KDD},
  year = 2009
}

@inproceedings{koutrika2007combating,
  address = {New York, NY, USA},
  author = {Koutrika, Georgia and Effendi, Frans Adjie and Gy\"{o}ngyi, Zolt\'{a}n and Heymann, Paul and Garcia-Molina, Hector},
  booktitle = {AIRWeb '07: Proceedings of the 3rd international workshop on Adversarial information retrieval on the web},
  doi = {http://doi.acm.org/10.1145/1244408.1244420},
  interhash = {8b6de1f035a46f5465f1ed868a18c79a},
  intrahash = {776b76b33d469e438b0e5f74fc7ec7f0},
  isbn = {978-1-59593-732-2},
  location = {Banff, Alberta, Canada},
  pages = {57--64},
  publisher = {ACM Press},
  title = {Combating spam in tagging systems},
  url = {http://portal.acm.org/citation.cfm?id=1244408.1244420},
  year = 2007
}

@article{1304546,
  abstract = {Social bookmarking services have recently gained popularity among Web users. Whereas numerous studies provide a historical account of tagging systems, the authors use their analysis of a domain-specific social bookmarking service called CiteULike to reflect on two metrics for evaluating tagging behavior: tag growth and tag reuse. They examine the relationship between these two metrics and articulate design implications for enhancing social bookmarking services. The authors also briefly reflect on their own work on developing a social bookmarking service for CiteSeer, an online scholarly digital library for computer science.},
  address = {Piscataway, NJ, USA},
  author = {Farooq, Umer and Song, Yang and Carroll, John M. and Giles, C. Lee},
  doi = {http://dx.doi.org/10.1109/MIC.2007.135},
  interhash = {13183e8fc4cbe0944a819afa2d9ff4eb},
  intrahash = {5785e8a8064b3d346f8c198c3c860bf6},
  issn = {1089-7801},
  journal = {IEEE Internet Computing},
  number = 6,
  pages = {29--35},
  publisher = {IEEE Educational Activities Department},
  title = {Social Bookmarking for Scholarly Digital Libraries},
  url = {http://portal.acm.org/citation.cfm?id=1304546&coll=Portal&dl=GUIDE&CFID=46454031&CFTOKEN=27530397},
  volume = 11,
  year = 2007
}

@inproceedings{jaeschke2009testingKDML,
  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 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 an evaluation of two exemplarily deployed recommendation methods, demonstrating the 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},
  interhash = {440fafda1eccf4036066f457eb6674a0},
  intrahash = {5e8f40e610e723e966676772aa205f80},
  month = sep,
  pages = {44 --51},
  title = {Testing and Evaluating Tag Recommenders in a Live System},
  url = {http://lwa09.informatik.tu-darmstadt.de/pub/KDML/WebHome/kdml09_R.Jaeschke_et_al.pdf},
  year = 2009
}

@article{limpens2009,
  address = {Los Alamitos, CA, USA},
  author = {Limpens, Freddy and Gandon, Fabien and Buffa, Michel},
  doi = {http://doi.ieeecomputersociety.org/10.1109/WI-IAT.2009.26},
  interhash = {89cdbfb24350947fd84ad88333b9022e},
  intrahash = {660821d34efd5432dd9324d1a12d1960},
  isbn = {978-0-7695-3801-3},
  journal = {Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on},
  pages = {132-135},
  publisher = {IEEE Computer Society},
  title = {Collaborative Semantic Structuring of Folksonomies},
  url = {http://www.computer.org/portal/web/csdl/doi/10.1109/WI-IAT.2009.26},
  volume = 1,
  year = 2009
}

@article{kim2009,
  abstract = {Websites that provide content creation and sharing features have become quite popular recently. These sites allow users to categorize and browse content using  tags' or free-text keyword topics. Since users contribute and tag social media content across a variety of social web platforms, creating new knowledge from distributed tag data has become a matter of performing various tasks, including publishing, aggregating, integrating, and republishing tag data. However, there are a number of issues in relation to data sharing and interoperability when processing tag data across heterogeneous tagging platforms. In this paper we introduce a semantic tag model that aims to explicitly offer the necessary structure, semantics and relationships between tags. This approach provides an improved opportunity for representing tag data in the form of reusable constructs at a semantic level. We also demonstrate a prototype that consumes and makes use of shared tag metadata across heterogeneous sources.
},
  author = {Kim, Hak-Lae and Decker, Stefan and Breslin, John G.},
  doi = {10.1177/0165551509346785},
  interhash = {89c42bc68404f0ba2b31d120de0123b8},
  intrahash = {f114b138bcb978a1cbad72e6af8b3fe2},
  journal = {Journal of Information Science},
  pages = 0165551509346785,
  title = {{Representing and sharing folksonomies with semantics}},
  url = {http://jis.sagepub.com/cgi/content/abstract/0165551509346785v1},
  year = 2009
}

@article{sofia2009semantic,
  abstract = {The usability and the strong social dimension of the Web2.0 applications has encouraged users to create, annotate and share
their content thus leading to a rich and content-intensive Web. Despite that, the Web2.0 content lacks the explicit semanticsthat would allow it to be used in large-scale intelligent applications. At the same time the advances in Semantic Web technologiesimply a promising potential for intelligent applications capable to integrate distributed content and knowledge from variousheterogeneous resources. We present FLOR a tool that performs semantic enrichment of folksonomy tagspaces by exploiting onlineontologies, thesauri and other knowledge sources.},
  author = {Angeletou, Sofia},
  interhash = {443ba65c503acef978d4127c46449824},
  intrahash = {432c85a94f5d181dd65dd386c8ddb6de},
  journal = {The Semantic Web - ISWC 2008},
  pages = {889--894},
  title = {Semantic Enrichment of Folksonomy Tagspaces},
  url = {http://dx.doi.org/10.1007/978-3-540-88564-1_58},
  year = 2009
}

@article{al-khalifa2007,
  author = {Al-Khalifa, Hend S. and Davis, Hugh C.},
  interhash = {3fbb87664648d1210a24667d8a75395a},
  intrahash = {c058fbfdcb787b442c8515bc9a87b8f6},
  title = {Exploring The Value Of Folksonomies For Creating Semantic Metadata},
  typesource = {Simple CitationSource},
  url = {http://eprints.ecs.soton.ac.uk/13555/},
  year = 2007
}

@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{JamesSinclair02012008,
  abstract = {The weighted list, known popularly as a `tag cloud', has appeared on many popular folksonomy-based web-sites. Flickr, Delicious, Technorati and many   others have all featured a tag cloud at some point in their history. However, it is unclear whether the tag cloud is actually useful as an aid to finding  information. We conducted an experiment, giving participants the option of using  a tag cloud or a traditional search interface to answer various questions. We found that where the information-seeking task required specific information, participants preferred the search interface. Conversely, where the   information-seeking task was more general, participants preferred the tag cloud. While the tag cloud is not without value, it is not sufficient as the sole means  of navigation for a folksonomy-based dataset.
},
  author = {Sinclair, James and Cardew-Hall, Michael},
  doi = {10.1177/0165551506078083},
  eprint = {http://jis.sagepub.com/cgi/reprint/34/1/15.pdf},
  interhash = {9781d30a620fe81d1b6b6b06925393ab},
  intrahash = {1cc0b296c0af7c80feea7b3bb1bf825c},
  journal = {Journal of Information Science},
  number = 1,
  pages = {15-29},
  title = {{The folksonomy tag cloud: when is it useful?}},
  url = {http://jis.sagepub.com/cgi/content/abstract/34/1/15},
  volume = 34,
  year = 2008
}

@inproceedings{conf/wsdm/WetzkerZBA10,
  author = {Wetzker, Robert and Zimmermann, Carsten and Bauckhage, Christian and Albayrak, Sahin},
  booktitle = {WSDM},
  crossref = {conf/wsdm/2010},
  date = {2010-02-18},
  editor = {Davison, Brian D. and Suel, Torsten and Craswell, Nick and Liu, Bing},
  ee = {http://doi.acm.org/10.1145/1718487.1718497},
  interhash = {12e89c88182a393dae8d63287f65540d},
  intrahash = {54d5f72f2993a1c60d3070782bac69ac},
  isbn = {978-1-60558-889-6},
  pages = {71-80},
  publisher = {ACM},
  title = {I tag, you tag: translating tags for advanced user models.},
  url = {http://dblp.uni-trier.de/db/conf/wsdm/wsdm2010.html#WetzkerZBA10},
  year = 2010
}

@inproceedings{1255198,
  abstract = {Social bookmarking is an emerging type of a Web service that helps users share, classify, and discover interesting resources. In this paper, we explore the concept of an enhanced search, in which data from social bookmarking systems is exploited for enhancing search in the Web. We propose combining the widely used link-based ranking metric with the one derived using social bookmarking data. First, this increases the precision of a standard link-based search by incorporating popularity estimates from aggregated data of bookmarking users. Second, it provides an opportunity for extending the search capabilities of existing search engines. Individual contributions of bookmarking users as well as the general statistics of their activities are used here for a new kind of a complex search where contextual, temporal or sentiment-related information is used. We investigate the usefulness of social bookmarking systems for the purpose of enhancing Web search through a series of experiments done on datasets obtained from social bookmarking systems. Next, we show the prototype system that implements the proposed approach and we present some preliminary results.},
  address = {New York, NY, USA},
  author = {Yanbe, Yusuke and Jatowt, Adam and Nakamura, Satoshi and Tanaka, Katsumi},
  booktitle = {JCDL '07: Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries},
  doi = {http://doi.acm.org/10.1145/1255175.1255198},
  interhash = {13ebfc0942b5908890c3caaa7046fe50},
  intrahash = {d896ae22bc7b52edefbfb9cdb373cf83},
  isbn = {978-1-59593-644-8},
  location = {Vancouver, BC, Canada},
  pages = {107--116},
  publisher = {ACM},
  title = {Can social bookmarking enhance search in the web?},
  url = {http://portal.acm.org/citation.cfm?id=1255198},
  year = 2007
}

@inproceedings{conf/wsdm/HeymannG09,
  author = {Heymann, Paul and Garcia-Molina, Hector},
  booktitle = {WSDM (Late Breaking-Results)},
  crossref = {conf/wsdm/2009},
  date = {2009-03-10},
  editor = {Baeza-Yates, Ricardo A. and Boldi, Paolo and Ribeiro-Neto, Berthier A. and Cambazoglu, Berkant Barla},
  ee = {http://www.wsdm2009.org/heymann_2009_tagging.pdf},
  interhash = {67ea8530c8f0fd5374d35264213d48aa},
  intrahash = {bbea77e3d3ce24dce7be0b3385889186},
  isbn = {978-1-60558-390-7},
  publisher = {ACM},
  title = {Contrasting Controlled Vocabulary and Tagging: Experts Choose the Right Names to Label the Wrong Things.},
  url = {http://dblp.uni-trier.de/db/conf/wsdm/wsdm2009.html#HeymannG09},
  year = 2009
}

@inproceedings{plangprasopchok2009,
  abstract = {Automatic folksonomy construction from tags has attracted much attention recently. However, inferring hierarchical relations between concepts from tags has a drawback in that it is difficult to distinguish between more popular and more general concepts. Instead of tags we propose to use user-specified relations for learning folksonomy. We explore two statistical frameworks for aggregating many shallow individual hierarchies, expressed through the collection/set relations on the social photosharing site Flickr, into a common deeper folksonomy that reflects how a community organizes knowledge. Our approach addresses a number of challenges that arise while aggregating information from diverse users, namely noisy vocabulary, and variations in the granularity level of the concepts expressed. Our second contribution is a method for automatically evaluating learned folksonomy by comparing it to a reference taxonomy, e.g., the Web directory created by the Open Directory Project. Our empirical results suggest that user-specified relations are a good source of evidence for learning folksonomies.},
  address = {New York, NY, USA},
  author = {Plangprasopchok, A. and Lerman, K.},
  booktitle = {WWW '09: Proceedings of the 18th international conference on World wide web},
  doi = {http://doi.acm.org/10.1145/1526709.1526814},
  interhash = {fccd894a82edb040d7438d6da91e3ebe},
  intrahash = {559ee9d48f1a510f56765b2357aa8ea5},
  isbn = {978-1-60558-487-4},
  location = {Madrid, Spain},
  pages = {781--790},
  publisher = {ACM},
  title = {Constructing folksonomies from user-specified relations on flickr},
  url = {http://www2009.org/proceedings/pdf/p781.pdf},
  year = 2009
}

@inproceedings{Zhou/2007/Unsupervised,
  abstract = {This paper deals with the problem of exploring hierarchical semantics from social annotations. Recently, social annotation services have become more and more popular in Semantic Web. It allows users to arbitrarily annotate web resources, thus, largely lowers the barrier to cooperation. Furthermore, through providing abundant meta-data resources, social annotation might become a key to the development of Semantic Web. However, on the other hand, social annotation has its own apparent limitations, for instance, 1) ambiguity and synonym phenomena and 2) lack of hierarchical information. In this paper, we propose an unsupervised model to automatically derive hierarchical semantics from social annotations. Using a social bookmark service Del.icio.us as example, we demonstrate that the derived hierarchical semantics has the ability to compensate those shortcomings. We further apply our model on another data set from Flickr to testify our model's applicability on different environments. The experimental results demonstrate our model's effciency.},
  address = {Berlin, Heidelberg},
  author = {Zhou, Mianwei and Bao, Shenghua and Wu, Xian and Yu, Yong},
  booktitle = {Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea},
  crossref = {http://data.semanticweb.org/conference/iswc-aswc/2007/proceedings},
  editor = {Aberer, Karl and Choi, Key-Sun and Noy, Natasha and Allemang, Dean and Lee, Kyung-Il and Nixon, Lyndon J B and Golbeck, Jennifer and Mika, Peter and Maynard, Diana and Schreiber, Guus and Cudré-Mauroux, Philippe},
  interhash = {af21595ee9f4a13b5e651ad049f31262},
  intrahash = {355fcbb32255f3ba5f41819c00c520ba},
  month = {November},
  pages = {673--686},
  publisher = {Springer Verlag},
  series = {LNCS},
  title = {An Unsupervised Model for Exploring Hierarchical Semantics from Social Annotations},
  url = {http://iswc2007.semanticweb.org/papers/673.pdf},
  volume = 4825,
  year = 2007
}

@inproceedings{specia07eswc,
  address = {Berlin Heidelberg, Germany},
  author = {Specia, L. and Motta, E.},
  booktitle = {Proc. of the European Semantic Web Conference (ESWC2007)},
  interhash = {b828fbd5c9ddc4f9551f973445ecb283},
  intrahash = {743087c4c80f3d06476083f2be43f6f1},
  month = {July},
  pages = {624-639},
  publisher = {Springer-Verlag},
  series = {LNCS},
  title = {Integrating Folksonomies with the Semantic Web},
  url = {http://people.kmi.open.ac.uk/motta/papers/SpeciaMotta_ESWC-2007_Final.pdf},
  volume = 4519,
  year = 2007
}