@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{illig2009comparison,
  abstract = {Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset. },
  address = {Berlin/Heidelberg},
  author = {Illig, Jens and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
  booktitle = {Knowledge Processing and Data Analysis},
  doi = {10.1007/978-3-642-22140-8_9},
  editor = {Wolff, Karl Erich and Palchunov, Dmitry E. and Zagoruiko, Nikolay G. and Andelfinger, Urs},
  interhash = {cd3420c0f73761453320dc528b3d1e14},
  intrahash = {f9d6e06ab0f2fdcebb77afa97d72e40a},
  isbn = {978-3-642-22139-2},
  pages = {136--149},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {A Comparison of Content-Based Tag Recommendations in Folksonomy Systems},
  url = {http://dx.doi.org/10.1007/978-3-642-22140-8_9},
  vgwort = {24},
  volume = 6581,
  year = 2011
}

@article{kaser2007tagcloud,
  abstract = {Tag clouds provide an aggregate of tag-usage statistics. They are typically sent as in-line HTML to browsers. However, display mechanisms suited for ordinary text are not ideal for tags, because font sizes may vary widely on a line. As well, the typical layout does not account for relationships that may be known between tags. This paper presents models and algorithms to improve the display of tag clouds that con- sist of in-line HTML, as well as algorithms that use nested tables to achieve a more general 2-dimensional layout in which tag relationships are considered. The first algorithms leverage prior work in typesetting and rectangle packing, whereas the second group of algorithms leverage prior work in Electronic Design Automation. Experiments show our algorithms can be efficiently implemented and perform well. },
  author = {Kaser, Owen and Lemire, Daniel},
  date = {2008-01-02},
  interhash = {cb6ed5e3340cf684ec55299adc65e1a9},
  intrahash = {56270d1311c066a3852bea23eeb8d484},
  journal = {CoRR},
  note = {informal publication},
  title = {Tag-Cloud Drawing: Algorithms for Cloud Visualization},
  url = {http://arxiv.org/abs/cs/0703109},
  volume = {abs/cs/0703109},
  year = 2007
}

@inproceedings{zanardi2008social,
  abstract = {Social (or folksonomic) tagging has become a very popular way to describe, categorise, search, discover and navigate content within Web 2.0 websites. Unlike taxonomies, which overimpose a hierarchical categorisation of content, folksonomies empower end users by enabling them to freely create and choose the categories (in this case, tags) that best describe some content. However, as tags are informally defined, continually changing, and ungoverned, social tagging has often been criticised for lowering, rather than increasing, the efficiency of searching, due to the number of synonyms, homonyms, polysemy, as well as the heterogeneity of users and the noise they introduce. In this paper, we propose Social Ranking, a method that exploits recommender system techniques to increase the efficiency of searches within Web 2.0. We measure users' similarity based on their past tag activity. We infer tags' relationships based on their association to content. We then propose a mechanism to answer a user's query that ranks (recommends) content based on the inferred semantic distance of the query to the tags associated to such content, weighted by the similarity of the querying user to the users who created those tags. A thorough evaluation conducted on the CiteULike dataset demonstrates that Social Ranking neatly improves coverage, while not compromising on accuracy.},
  address = {New York, NY, USA},
  author = {Zanardi, Valentina and Capra, Licia},
  booktitle = {RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems},
  doi = {http://doi.acm.org/10.1145/1454008.1454018},
  interhash = {dcf815f49a37bf32408fd66ae77d85c3},
  intrahash = {e9e606a98ce7f2fed11c339a500a2f88},
  isbn = {978-1-60558-093-7},
  location = {Lausanne, Switzerland},
  pages = {51--58},
  publisher = {ACM},
  title = {Social ranking: uncovering relevant content using tag-based recommender systems},
  url = {http://portal.acm.org/citation.cfm?id=1454008.1454018},
  year = 2008
}

@inproceedings{shepitsen2008personalized,
  abstract = {Collaborative tagging applications allow Internet users to annotate resources with personalized tags. The complex network created by many annotations, often called a folksonomy, permits users the freedom to explore tags, resources or even other user's profiles unbound from a rigid predefined conceptual hierarchy. However, the freedom afforded users comes at a cost: an uncontrolled vocabulary can result in tag redundancy and ambiguity hindering navigation. Data mining techniques, such as clustering, provide a means to remedy these problems by identifying trends and reducing noise. Tag clusters can also be used as the basis for effective personalized recommendation assisting users in navigation. We present a personalization algorithm for recommendation in folksonomies which relies on hierarchical tag clusters. Our basic recommendation framework is independent of the clustering method, but we use a context-dependent variant of hierarchical agglomerative clustering which takes into account the user's current navigation context in cluster selection. We present extensive experimental results on two real world dataset. While the personalization algorithm is successful in both cases, our results suggest that folksonomies encompassing only one topic domain, rather than many topics, present an easier target for recommendation, perhaps because they are more focused and often less sparse. Furthermore, context dependent cluster selection, an integral step in our personalization algorithm, demonstrates more utility for recommendation in multi-topic folksonomies than in single-topic folksonomies. This observation suggests that topic selection is an important strategy for recommendation in multi-topic folksonomies.},
  address = {New York, NY, USA},
  author = {Shepitsen, Andriy and Gemmell, Jonathan and Mobasher, Bamshad and Burke, Robin},
  booktitle = {RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems},
  doi = {10.1145/1454008.1454048},
  interhash = {c9028129dd7cd8314673bd64cbb6198e},
  intrahash = {a7552f8d8d5db4f867ae6e94e1a4442f},
  isbn = {978-1-60558-093-7},
  location = {Lausanne, Switzerland},
  pages = {259--266},
  publisher = {ACM},
  title = {Personalized recommendation in social tagging systems using hierarchical clustering},
  url = {http://portal.acm.org/citation.cfm?id=1454008.1454048},
  year = 2008
}

@inproceedings{garg2008personalized,
  abstract = {We study the problem of personalized, interactive tag recommendation for Flickr: While a user enters/selects new tags for a particular picture, the system suggests related tags to her, based on the tags that she or other people have used in the past along with (some of) the tags already entered. The suggested tags are dynamically updated with every additional tag entered/selected. We describe a new algorithm, called Hybrid, which can be applied to this problem, and show that it outperforms previous algorithms. It has only a single tunable parameter, which we found to be very robust.

Apart from this new algorithm and its detailed analysis, our main contributions are (i) a clean methodology which leads to conservative performance estimates, (ii) showing how classical classification algorithms can be applied to this problem, (iii) introducing a new cost measure, which captures the effort of the whole tagging process, (iv) clearly identifying, when purely local schemes (using only a user's tagging history) can or cannot be improved by global schemes (using everybody's tagging history).},
  address = {New York, NY, USA},
  author = {Garg, Nikhil and Weber, Ingmar},
  booktitle = {RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems},
  doi = {10.1145/1454008.1454020},
  interhash = {97a0bfae393c50cc3b59e30ce2194dbe},
  intrahash = {bc41bb2e724191661fa99cb016809c37},
  isbn = {978-1-60558-093-7},
  location = {Lausanne, Switzerland},
  pages = {67--74},
  publisher = {ACM},
  title = {Personalized, interactive tag recommendation for flickr},
  url = {http://portal.acm.org/citation.cfm?id=1454020},
  year = 2008
}

@inproceedings{heymann2008social,
  abstract = {In this paper, we look at the "social tag prediction" problem. Given a set of objects, and a set of tags applied to those objects by users, can we predict whether a given tag could/should be applied to a particular object? We investigated this question using one of the largest crawls of the social bookmarking system del.icio.us gathered to date. For URLs in del.icio.us, we predicted tags based on page text, anchor text, surrounding hosts, and other tags applied to the URL. We found an entropy-based metric which captures the generality of a particular tag and informs an analysis of how well that tag can be predicted. We also found that tag-based association rules can produce very high-precision predictions as well as giving deeper understanding into the relationships between tags. Our results have implications for both the study of tagging systems as potential information retrieval tools, and for the design of such systems.},
  address = {New York, NY, USA},
  author = {Heymann, Paul and Ramage, Daniel and Garcia-Molina, Hector},
  booktitle = {SIGIR '08: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
  doi = {http://doi.acm.org/10.1145/1390334.1390425},
  interhash = {bb9455c80cc9bd8cf95c951a1318dabc},
  intrahash = {0e6023e192f539fe4fce9894b1fbca5a},
  isbn = {978-1-60558-164-4},
  location = {Singapore, Singapore},
  pages = {531--538},
  publisher = {ACM},
  title = {Social tag prediction},
  url = {http://portal.acm.org/citation.cfm?id=1390334.1390425},
  year = 2008
}

@inproceedings{1454017,
  address = {New York, NY, USA},
  author = {Symeonidis, Panagiotis and Nanopoulos, Alexandros and Manolopoulos, Yannis},
  booktitle = {RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems},
  doi = {http://doi.acm.org/10.1145/1454008.1454017},
  interhash = {8ee38f4ffc05845fcb98f121fb265d48},
  intrahash = {e93afe409833a632af02290bbe134cba},
  isbn = {978-1-60558-093-7},
  location = {Lausanne, Switzerland},
  pages = {43--50},
  publisher = {ACM},
  title = {Tag recommendations based on tensor dimensionality reduction},
  url = {http://portal.acm.org/citation.cfm?id=1454017},
  year = 2008
}

@inproceedings{yanfei2006cubic,
  abstract = {Personalized recommendation is used to conquer the information overload problem, and collaborative filtering recommendation (CF) is one of the most successful recommendation techniques to date. However, CF becomes less effective when users have multiple interests, because users have similar taste in one aspect may behave quite different in other aspects. Information got from social bookmarking websites not only tells what a user likes, but also why he or she likes it. This paper proposes a division algorithm and a CubeSVD algorithm to analysis this information, distill the interrelations between different users’ various interests, and make better personalized recommendation based on them. Experiment reveals the superiority of our method over traditional CF methods.
ER  -},
  author = {Xu, Yanfei and Zhang, Liang and Liu, Wei},
  booktitle = {APWeb},
  editor = {Zhou, Xiaofang and Li, Jianzhong and Shen, Heng Tao and Kitsuregawa, Masaru and Zhang, Yanchun},
  ee = {http://dx.doi.org/10.1007/11610113_66},
  interhash = {edf999afa5a0ff81e53b0c859b466659},
  intrahash = {98dd99b5f4189c8427163fd5a7568e1d},
  isbn = {3-540-31142-4},
  journal = {Frontiers of WWW Research and Development - APWeb 2006},
  pages = {733--738},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Cubic Analysis of Social Bookmarking for Personalized Recommendation},
  url = {http://dx.doi.org/10.1007/11610113_66},
  volume = 3841,
  year = 2006
}

@inproceedings{sen2006tagging,
  abstract = {A tagging community's vocabulary of tags forms the basis for social navigation and shared expression.We present a user-centric model of vocabulary evolution in tagging communities based on community influence and personal tendency. We evaluate our model in an emergent tagging system by introducing tagging features into the MovieLens recommender system.We explore four tag selection algorithms for displaying tags applied by other community members. We analyze the algorithms 'effect on vocabulary evolution, tag utility, tag adoption, and user satisfaction.},
  address = {New York, NY, USA},
  author = {Sen, Shilad and Lam, Shyong K. and Rashid, Al Mamunur and Cosley, Dan and Frankowski, Dan and Osterhouse, Jeremy and Harper, F. Maxwell and Riedl, John},
  booktitle = {CSCW '06: Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work},
  doi = {http://doi.acm.org/10.1145/1180875.1180904},
  interhash = {96b20bffcbc91e528461529935524b90},
  intrahash = {582641c05e7a0b9396945a951822c83f},
  isbn = {1-59593-249-6},
  location = {Banff, Alberta, Canada},
  pages = {181--190},
  publisher = {ACM},
  title = {tagging, communities, vocabulary, evolution},
  url = {http://portal.acm.org/citation.cfm?id=1180904},
  year = 2006
}

@article{sinclair:ftc,
  author = {Sinclair, J. and Cardew-Hall, M.},
  interhash = {fe7fb4aad79ca5ee3ba8a5b2e1c3cd5b},
  intrahash = {539fe40eb8dd2597956cae27d6fb02ac},
  journal = {Journal of Information Science},
  pages = 016555150607808,
  publisher = {CILIP},
  title = {{The folksonomy tag cloud: When is it useful?}},
  year = 2007
}

@article{cls_yulesimon,
  abstract = {The Yule-Simon model has been used as a tool to describe the growth of diverse systems, acquiring a paradigmatic character in many fields of research. Here we study a modified Yule-Simon model that takes into account the full history of the system by means of a hyperbolic memory kernel. We show how the memory kernel changes the properties of preferential attachment and provide an approximate analytical solution for the frequency distribution density as well as for the frequency-rank distribution.},
  author = {Cattuto, Ciro and Loreto, Vittorio and Servedio, Vito D.P.},
  interhash = {e1dbe404fff4f827f443889685ce83f1},
  intrahash = {d9fd1ea1b4a9ffdaf68332409cf90b6e},
  journal = {Europhysics Letters},
  number = 2,
  pages = {208-214},
  title = {A Yule-Simon process with memory},
  url = {http://www.iop.org/EJ/article/0295-5075/76/2/208/epl9598.html},
  volume = 76,
  year = 2006
}

@misc{golder05structure,
  author = {Golder, Scott and Huberman, Bernardo A.},
  citeulike-article-id = {305755},
  eprint = {cs.DL/0508082},
  interhash = {2d312240f16eba52c5d73332bc868b95},
  intrahash = {f852d7a909fa3edceb04abb7d2a20f71},
  month = Aug,
  priority = {2},
  title = {The Structure of Collaborative Tagging Systems},
  url = {http://arxiv.org/abs/cs.DL/0508082},
  year = 2005
}

@inproceedings{hassanmontero2006improving,
  address = {Merida, Spain},
  author = {Hassan-Montero, Y. and Herrero-Solana, V.},
  booktitle = {Proc. InSciT 2006},
  day = {25--28},
  interhash = {4458142370e3c6a4fe656af2f822a0dc},
  intrahash = {99ffb0c3a76afe508f5ff6b219f72515},
  month = Oct,
  title = {{Improving Tag-Clouds as Visual Information Retrieval Interfaces}},
  year = 2006
}

@article{gt06folksonomies,
  author = {Guy, Marieke and Tonkin, Emma},
  doi = {10.1045/january2006-guy},
  interhash = {535e0aea1bcbd7feb85a7495f284a589},
  intrahash = {a62decf2da83f2d9e10ff7846296699b},
  journal = {D-Lib Magazine},
  month = {January},
  note = {ISSN 1082-9873},
  number = 1,
  title = {Folksonomies - Tidying up Tags?},
  url = {http://dlib.org/dlib/january06/guy/01guy.html},
  volume = 12,
  year = 2006
}

@inproceedings{dkmnrt06visualizing,
  author = {Dubinko, M. and Kumar, R. and Magnani, J. and Novak, J. and Raghavan, P. and Tomkins, A.},
  booktitle = {Proceedings of the 15th International WWW Conference},
  day = {23-25},
  interhash = {b9ff2f72831a1406013a86c8202d6276},
  intrahash = {dc72abb1df242c52bf2c4fa19790dcec},
  month = May,
  title = {Visualizing Tags over Time},
  year = 2006
}

@inproceedings{dkmnrt06visualizing,
  author = {Dubinko, M. and Kumar, R. and Magnani, J. and Novak, J. and Raghavan, P. and Tomkins, A.},
  booktitle = {Proceedings of the 15th International WWW Conference},
  day = {23-26},
  interhash = {b9ff2f72831a1406013a86c8202d6276},
  intrahash = {dc72abb1df242c52bf2c4fa19790dcec},
  month = May,
  title = {Visualizing Tags over Time},
  url = {http://www2006.org/programme/item.php?id=25},
  year = 2006
}