@misc{citeulike:341233,
  abstract = {The investigation of community structures in networks is an important issue
in many domains and disciplines. This problem is relevant for social tasks
(objective analysis of relationships on the web), biological inquiries
(functional studies in metabolic, cellular or protein networks) or
technological problems (optimization of large infrastructures). Several types
of algorithm exist for revealing the community structure in networks, but a
general and quantitative definition of community is still lacking, leading to
an intrinsic difficulty in the interpretation of the results of the algorithms
without any additional non-topological information. In this paper we face this
problem by introducing two quantitative definitions of community and by showing
how they are implemented in practice in the existing algorithms. In this way
the algorithms for the identification of the community structure become fully
self-contained. Furthermore, we propose a new local algorithm to detect
communities which outperforms the existing algorithms with respect to the
computational cost, keeping the same level of reliability. The new algorithm is
tested on artificial and real-world graphs. In particular we show the
application of the new algorithm to a network of scientific collaborations,
which, for its size, can not be attacked with the usual methods. This new class
of local algorithms could open the way to applications to large-scale
technological and biological applications.},
  author = {Radicchi, Filippo and Castellano, Claudio and Cecconi, Federico and Loreto, Vittorio and Parisi, Domenico},
  citeulike-article-id = {341233},
  comment = {"In general algorithms define communities operationally as what the they finds. A dendrogram, i. e. a community
structure, is always produced by the algorithms down to the level of single nodes, independently from the type
of graph analyzed. This is due to the lack of explicit prescriptions to discriminate between networks that
are actually endowed with a community structure and those that are not. As a consequence, in practical
applications one needs additional, non topological, information on the nature of the network to understand
which of the branches of the tree have a real significance. Without such information it is not clear at all whether
the identification of a community is reliable or not."

---

Domain: scientific collaborations

Task: calculate a dendrogram (the community graph)

Method: effucuebt GN (Girvan \& Newman( algorithm based on edge betweenness. Their algorithm allows to be fine-tuned beween acting local or global. To be more efficient they replace the "edge betweenness" by "edge clustering coefficient" which is based on the number of triangles the edge is contained in VS the degree of the incident nodes.

Motto: "Algorithm must include the quantitative community definition"},
  eprint = {cond-mat/0309488},
  interhash = {6ec9b00862909de405c08db1c9b43d63},
  intrahash = {8634d935e0bf4d74a870d5c805612665},
  month = Feb,
  priority = {0},
  title = {Defining and identifying communities in networks},
  url = {http://arxiv.org/abs/cond-mat/0309488},
  year = 2004
}

@inproceedings{1135839,
  address = {New York, NY, USA},
  author = {Wu, Xian and Zhang, Lei and Yu, Yong},
  booktitle = {WWW '06: Proceedings of the 15th international conference on World Wide Web},
  doi = {http://doi.acm.org/10.1145/1135777.1135839},
  interhash = {478741551c92402f539a90a9caed61b6},
  intrahash = {2ff38a7f8e9e3941d0598877fe964eb5},
  isbn = {1-59593-323-9},
  location = {Edinburgh, Scotland},
  pages = {417--426},
  publisher = {ACM Press},
  title = {Exploring social annotations for the semantic web},
  year = 2006
}

@inproceedings{hotho2006folkrank,
  abstract = { In social bookmark tools users are setting up
lightweight conceptual structures called folksonomies. Currently,
the information retrieval support is limited. We present a formal
model and a new search algorithm for folksonomies, called
FolkRank, that exploits the structure of the folksonomy. The
proposed algorithm is also applied to find communities within the
folksonomy and is used to structure search results. All findings are
demonstrated on a large scale dataset. A long version of this paper
has been published at the European Semantic Web Conference
2006.},
  author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd},
  booktitle = {Proc. FGIR 2006},
  interhash = {3468dc3fed17eadf2e7c6ff06fbb34a3},
  intrahash = {4d8b4f79814691fbe6db8357d63206a1},
  issn = {0941-3014},
  pages = {111-114},
  title = {FolkRank: A Ranking Algorithm for Folksonomies},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006folkrank.pdf},
  vgwort = {8},
  year = 2006
}

@inproceedings{hjss06bibsonomy,
  author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd},
  booktitle = {Proceedings of the Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures},
  interhash = {d28c9f535d0f24eadb9d342168836199},
  intrahash = {a6faae355d867b5f998ed30e79eec3cf},
  isbn = {87-7307-769-0},
  pages = {87-102},
  title = {{BibSonomy}: A Social Bookmark and Publication Sharing System},
  url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2006/iccs_tools_ws_final.pdf},
  vgwort = {27},
  year = 2006
}

@inproceedings{bunescu-etal-2006,
  author = {Bunescu, Razvan and Pasca, Marius},
  booktitle = {Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL-06), Trento, Italy},
  interhash = {f1e2bfaf83e0af0d90b42c8f70394fa4},
  intrahash = {cc98e8aa7e3fb7d8addc0ec4fe45f7d2},
  month = {April},
  pages = {9-16},
  title = {Using Encyclopedic Knowledge for Named Entity Disambiguation },
  url = {http://www.cs.utexas.edu/~ml/publication/paper.cgi?paper=encyc-eacl-06.ps.gz},
  year = 2006
}

@inproceedings{jaeschke06wege,
  address = {Halle-Wittenberg},
  author = {Jäschke, Robert and Hotho, Andreas and Schmitz, Christoph and Stumme, Gerd},
  booktitle = {Proc. 18. Workshop Grundlagen von Datenbanken},
  editor = {Braß, Stefan and Hinneburg, Alexander},
  interhash = {59224b5889a24108434a9b5ecc6b0887},
  intrahash = {2b6be3bd5daee7119973fcf69909956f},
  month = {June},
  pages = {80-84},
  publisher = {Martin-Luther-Universität },
  title = {Wege zur Entdeckung von Communities in Folksonomies},
  year = 2006
}

@inproceedings{Mika2005,
  author = {Mika, Peter},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  booktitle = {International Semantic Web Conference},
  doi = {10.1007/11574620_38},
  ee = {http://dx.doi.org/10.1007/11574620_38},
  interhash = {5ea12110b5bb0e3a8ad09aeb16a70cdb},
  intrahash = {6929599cc8fbdc408282907eeec37204},
  owner = {mlux},
  pages = {522-536},
  publisher = {Springer},
  series = {LNCS},
  timestamp = {2006.01.19},
  title = {Ontologies Are Us: A Unified Model of Social Networks and Semantics},
  url = {http://www.cs.vu.nl/~pmika/research/papers/ISWC-folksonomy.pdf},
  year = 2005
}

@book{surowiecki2004,
  author = {Surowiecki, J.},
  interhash = {18c796739b7555d3aad50d0db1ea0af1},
  intrahash = {b78f7a737aa99ffad5d1862f8ef478ae},
  publisher = {Doubleday},
  title = {The wisdom of crowds},
  year = 2004
}

@unpublished{vanderwal2005folksonomy,
  author = {{Vander Wal}, Thomas},
  day = 2,
  interhash = {420d4351fd66785ee42a7ce108c1b957},
  intrahash = {8b803d69dc18f53283074f2b285ab94f},
  month = nov,
  title = {Folksonomy Definition and Wikipedia},
  url = {\url{http://www.vanderwal.net/random/entrysel.php?blog=1750}},
  year = 2005
}

@misc{voss-2007,
  abstract = {This paper gives an overview of current trends in manual indexing on the Web. Along with a general rise of user generated content there are more and more tagging systems that allow users to annotate digital resources with tags (keywords) and share their annotations with other users. Tagging is frequently seen in contrast to traditional knowledge organization systems or as something completely new. This paper shows that tagging should better be seen as a popular form of manual indexing on the Web. Difference between controlled and free indexing blurs with sufficient feedback mechanisms. A revised typology of tagging systems is presented that includes different user roles and knowledge organization systems with hierarchical relationships and vocabulary control. A detailed bibliography of current research in collaborative tagging is included.},
  author = {Voss, Jakob},
  interhash = {c293cd6ef590c6aaf32df75cbdb9de82},
  intrahash = {9ec98351dc630ea6b1f65046ba44a8dd},
  title = {Tagging, Folksonomy \& Co - Renaissance of Manual Indexing?},
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cs/0701072},
  year = 2007
}

@techreport{citeulike:739394,
  abstract = {Collaborative tagging systems---systems where many casual users annotate objects with free-form strings (tags) of their choosing---have recently emerged as a powerful way to label and organize large collections of data. During our recent investigation into these types of systems, we discovered a simple but remarkably effective algorithm for converting a large corpus of tags annotating objects in a tagging system into a navigable hierarchical taxonomy of tags. We first discuss the algorithm and then present a preliminary model to explain why it is so effective in these types of systems.},
  author = {Heymann, Paul and Garcia-Molina, Hector},
  citeulike-article-id = {739394},
  institution = {Computer Science Department},
  interhash = {d77846b40aadb0e25233cabf905bb93e},
  intrahash = {3b4ce6fd7fa6dbf1c39fd261fa39fcd6},
  month = {April},
  number = {2006-10},
  priority = {3},
  school = {Standford University},
  title = {Collaborative Creation of Communal Hierarchical Taxonomies in Social Tagging Systems},
  url = {http://dbpubs.stanford.edu:8090/pub/2006-10},
  year = 2006
}

@article{tonkin2006Folksonomies,
  author = {Tonkin, Emma and Guy, Marieke},
  date = {January 2006},
  interhash = {535e0aea1bcbd7feb85a7495f284a589},
  intrahash = {9488117bf156fe15b2fb3b8ab4376dec},
  journal = {D-Lib},
  title = {Folksonomies: Tidying Up Tags?},
  volume = {volume 12(1)},
  year = 2006
}

@inproceedings{schmitz06,
  author = {Schmitz, Patrick},
  booktitle = {Collaborative Web Tagging Workshop at WWW2006, Edinburgh, Scotland},
  interhash = {1335f4ef87f951e6edf4fd94f885d3a2},
  intrahash = {77143fd854a06583430afae1371fad71},
  month = May,
  title = {Inducing Ontology from Flickr Tags.},
  url = {http://www.ibiblio.org/www_tagging/2006/22.pdf},
  year = 2006
}

@misc{citeulike:1115448,
  abstract = {Tagging, folksonomy, distributed classification, ethnoclassification—however it is labelled, the concept of users creating and aggregating their own metadata is gaining ground on the internet. This literature review briefly defines the topic at hand, looking at current implementations and summarizing key advantages and disadvantages of distributed classification systems with reference to prominent folksonomy commentators.  After considering whether distributed classification can replace expert catalogers entirely, it concludes that distributed classification can make an important contribution to digital information organisation, but that it may need to be integrated with more traditional organisation tools to overcome its current weaknesses.},
  author = {Speller, Edith},
  citeulike-article-id = {1115448},
  interhash = {2d40636857edfcf58b0977948e40ce4d},
  intrahash = {16373fb33b38c4769794ed7e7875f27e},
  journal = {Library Student Journal},
  month = {February},
  priority = {0},
  title = {Library Student Journal: Collaborative tagging, folksonomies, distributed classification or ethnoclassification: a literature review.},
  url = {http://informatics.buffalo.edu/org/lsj/articles/speller_2007_2_collaborative.php},
  year = 2007
}

@inproceedings{HWLK06,
  author = {Han, Peng and Wang, Zhimei and Li, Zhiyun and Kramer, Bernd and Yang, Fan},
  booktitle = {Web Intelligence},
  crossref = {conf/webi/2006},
  date = {2007-01-10},
  ee = {http://doi.ieeecomputersociety.org/10.1109/WI.2006.162},
  interhash = {caf35361720e0ccb4aa74169646cc606},
  intrahash = {b652b0d73b964ebc282c3c3060c01c9d},
  isbn = {0-7695-2747-7},
  pages = {757-760},
  publisher = {IEEE Computer Society},
  title = {Substitution or Complement: An Empirical Analysis on the Impact of Collaborative Tagging on Web Search.},
  url = {http://dblp.uni-trier.de/db/conf/webi/webi2006.html#HanWLKY06},
  year = 2006
}

@inproceedings{1128138,
  address = {Washington, DC, USA},
  author = {Niwa, Satoshi and Doi, Takuo and Honiden, Shinichi},
  booktitle = {ITNG '06: Proceedings of the Third International Conference on Information Technology: New Generations (ITNG'06)},
  doi = {http://dx.doi.org/10.1109/ITNG.2006.140},
  interhash = {9554b0b80885f6c6f8d4f63a88d7b215},
  intrahash = {5b250ba1a970964b09b8f7d0e395b949},
  isbn = {0-7695-2497-4},
  pages = {388--393},
  publisher = {IEEE Computer Society},
  title = {Web Page Recommender System based on Folksonomy Mining},
  url = {http://portal.acm.org/citation.cfm?id=1128138},
  year = 2006
}

@inproceedings{marlow2006position,
  abstract = {In recent years, tagging systems have become increasingly popular. These systems enable users to add keywords (i.e., “tags”) to Internet resources (e.g., web pages, images, videos) without relying on a controlled vocabulary. Tagging systems have the potential to improve search, spam detection, reputation systems, and personal organization while introducing new modalities of social communication and opportunities for data mining. This potential is largely due to the social structure that underlies many of the current systems. Despite the rapid expansion of applications that support tagging of resources, tagging systems are still not well studied or understood. In this paper, we provide a short description of the academic related work to date. We offer a model of tagging systems, specifically in the context of web-based systems, to help us illustrate the possible benefits of these tools. Since many such systems already exist, we provide a taxonomy of tagging systems to help inform their analysis and design, and thus enable researchers to frame and compare evidence for the sustainability of such systems. We also provide a simple taxonomy of incentives and contribution models to inform potential evaluative frameworks. While this work does not present comprehensive empirical results, we present a preliminary study of the photosharing and tagging system Flickr to demonstrate our model and explore some of the issues in one sample system. This analysis helps us outline and motivate possible future directions of research in tagging systems.},
  author = {Marlow, Cameron and Naaman, Mor and Boyd, Danah and Davis, Marc},
  booktitle = {Collaborative Web Tagging Workshop at WWW2006},
  interhash = {7446351e0d902ee4f36fb750f82c50a5},
  intrahash = {8b100f88154692615b1e31e2e243e78c},
  location = {Edinburgh, Scotland},
  month = May,
  title = {{Position Paper, Tagging, Taxonomy, Flickr, Article, ToRead}},
  url = {http://www.danah.org/papers/WWW2006.pdf},
  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
}

@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{mazzocchi2005folksologies,
  author = {Mazzocchi, Stefano},
  day = 5,
  interhash = {93991e09308c555937879c9c47187f26},
  intrahash = {26c422a5cc1d519f94d662f265488c09},
  month = apr,
  title = {Folksologies: de-idealizing ontologies},
  url = {http://www.betaversion.org/~stefano/linotype/news/85/},
  year = 2005
}

@misc{michail2005collaborativerank,
  author = {Michail, Amir},
  day = 23,
  howpublished = {\url{http://collabrank.web.cse.unsw.edu.au/collabrank.pdf}},
  institution = {School of Computer Science and Engineering},
  interhash = {792c8f9b881af577e8e7e0d488562951},
  intrahash = {876a732f49e2a5e44b0df3fe5c6fe2a1},
  month = {April},
  note = {(work in progress)},
  title = {{CollaborativeRank: Motivating People to Give Helpful and Timely Ranking Suggestions}},
  url = {\url{http://collabrank.web.cse.unsw.edu.au/collabrank.pdf}},
  year = 2005
}

@unpublished{szekely2006ranking,
  author = {Szekely, Benjamin and Torres, Elias},
  interhash = {3c31525eac065856391242454cdcf7a6},
  intrahash = {2d307d46e596d58844014895928051dc},
  month = may,
  title = {Ranking Bookmarks and Bistros: Intelligent Community and Folksonomy Development},
  url = {http://torrez.us/archives/2005/07/13/tagrank.pdf},
  year = 2005
}

@inproceedings{freyne07,
  address = {New York, NY, USA},
  author = {Freyne, Jill and Farzan, Rosta and Brusilovsky, Peter and Smyth, Barry and Coyle, Maurice},
  booktitle = {IUI '07: Proceedings of the 12th international conference on Intelligent user interfaces},
  doi = {http://doi.acm.org/10.1145/1216295.1216312},
  interhash = {871e012dc7b1c131d32480f1e3a655e7},
  intrahash = {88603ee0903b30dc642aebdaa6a22f93},
  isbn = {1-59593-481-2},
  location = {Honolulu, Hawaii, USA},
  pages = {52--61},
  publisher = {ACM Press},
  title = {Collecting community wisdom: integrating social search \& social navigation},
  url = {http://portal.acm.org/citation.cfm?id=1216312},
  year = 2007
}

@inproceedings{xfms06towards,
  author = {Xu, Zhichen and Fu, Yun and Mao, Jianchang and Su, Difu},
  booktitle = {Proceedings of the Collaborative Web Tagging Workshop at the WWW 2006},
  interhash = {e18fd92b0ffa21b9f0cbb3a2fe15b873},
  intrahash = {49719d13c6da0c5f6917b97ef777184e},
  title = {Towards the Semantic Web: Collaborative Tag Suggestions},
  year = 2006
}

@article{hammond2005sbt,
  author = {Hammond, T. and Hannay, T. and Lund, B. and Scott, J.},
  interhash = {7d45bfed52477dab1181188b70e0c47a},
  intrahash = {e9766a322c9f790909afcd5263e8e710},
  journal = {D-Lib Magazine},
  title = {{Social Bookmarking Tools (I)}},
  year = 2005
}

@article{lhfh05social,
  author = {Lund, Ben and Hammond, Tony and Flack, Martin and Hannay, Timo},
  interhash = {46c0a98ab6ccb96ff4722f35781807de},
  intrahash = {13958ef5da2d2133b9b84e9a3cb40da1},
  journal = {D-Lib Magazine},
  month = {April},
  number = 4,
  organization = {{N}ature {P}ublishing {G}roup},
  title = {{S}ocial {B}ookmarking {T}ools ({II}): {A} {C}ase {S}tudy - {C}onnotea},
  volume = 11,
  year = 2005
}

@inproceedings{michlmayr07,
  author = {Michlmayr, Elke and Cayzer, Steve},
  booktitle = {Tagging and Metadata for Social Information Organization Workshop, WWW07},
  interhash = {506fd3e53236139f1267941370f58fd1},
  intrahash = {e48f3552d61c3ac40bd38e73e21d1a91},
  title = {Learning User Profiles from Tagging Data and Leveraging them for Personal(ized) Information Access},
  year = 2007
}

@mastersthesis{bielenberg2005gss,
  author = {Bielenberg, K.},
  interhash = {3d01034c62a397cd2ed6ccb3b3a40a6e},
  intrahash = {dd714f84969ebcb7436b3fa08b8993e2},
  title = {{Groups in Social Software: Utilizing Tagging to Integrate Individual Contexts for Social Navigation}},
  year = 2005
}

@article{ieKey,
  abstract = {Empfehlungssysteme tragen Inhalte individuell
an Nutzer im WWW heran,
basierend auf deren konkreten Bedürfnissen,
Vorlieben und Interessen. Solche
Systeme können Produkte, Services,
Nutzer (mit analogen Interessen) uvm.
vorschlagen und stellen damit – gerade
im Web 2.0-Zeitalter – eine besondere
Form der Personalisierung sowie des
social networking dar. Damit bieten
Empfehlungssysteme Anbietern im ECommerce
einen entscheidenden
Marktvorteil, weshalb die Auswertung
der Kundendaten bei großen Firmen
wie Amazon, Google oder Ebay eine
hohe Priorität besitzt. Aus diesem
Grund wird im vorliegenden Artikel auf
die Ansätze von Empfehlungssystemen,
welche auf unterschiedliche Weise
die Bedürfnisse des Nutzers aufgreifen
bzw. „vorausahnen“ und ihm Vorschläge
(aus verschiedenen Bereichen)
unterbreiten können, eingegangen. Der
Artikel liefert eine Definition und Darstellung
der Arbeitsweisen von Empfehlungssystemen.
Dabei werden die
verschiedenen Methodiken jener
Dienste vergleichend erläutert, um ihre
jeweiligen Vor- und Nachteile deutlich
zu machen. Außerdem wird der Ontologie-
und Folksonomy-Einsatz innerhalb
von Empfehlungssystemen beleuchtet,
um Chancen und Risiken der Anwendung
von Methoden der Wissensrepräsentation
für zukünftige Forschungsarbeiten
einschätzen zu können.
Recommender Systems in an Information
Science View – The State of the Art
Recommender systems offer content
individually to users in the WWW,
based on their concrete needs, preferences
and interests. Those systems
can propose products, services, users
(with analogous interests), etc.) and
represent a special form of personalisation
as well as of social networking
– exactly in the Web 2.0 age. Recommender
systems offer e.g. suppliers in
the e-commerce a crucial market advantage.
So, the evaluation of the customer
data has high priority at big
companies like Amazon, Google or
Ebay. For this reason we engaged in
recommender systems, which take up
the user’s needs in different ways, to
“anticipate“ needs and make suggestions
(from different areas) to the user.
This review article achieves a definition
and representation of operations
and methods of recommender systems.
Exactly the different methodologies
of those services should be expounded
comparativly on that occasion
in order to represent advantages
and disadvantages. The use of ontologies
and folksonomies as implementations
in recommender systems is portrayed
in order to be able to take into
consideration chances and risks of the
application of knowledge representation
methods for future researches.},
  author = {Höhfeld, Stefanie and Kwiatkowski, Melanie},
  interhash = {e6216d6334aad0ebcb7190eb661c83f6},
  intrahash = {501849ade58a25831e72519f6add1313},
  journal = {IWP-Information Wissenschaft & Praxis},
  number = 5,
  pages = {265-276},
  title = {Empfehlungssysteme aus informationswissenschaftlicher Sicht-State of the Art},
  url = {http://wwwalt.phil-fak.uni-duesseldorf.de/infowiss/admin/public_dateien/files/58/1189509550empfehlung.pdf},
  volume = 58,
  year = 2007
}

@inproceedings{schmitz2007network,
  address = {Banff},
  author = {Schmitz, Christoph and Grahl, Miranda and Hotho, Andreas and Stumme, Gerd and Catutto, Ciro and Baldassarri, Andrea and Loreto, Vittorio and Servedio, Vito D. P.},
  booktitle = {Proc. WWW2007 Workshop ``Tagging and Metadata for Social Information Organization''},
  day = 8,
  interhash = {20bd468c1c9b71206ac6f8b67ed676d6},
  intrahash = {23a0a0cd67ab0014e0346527e986caeb},
  month = May,
  title = {Network Properties of Folksonomies},
  year = 2007
}

@article{keyhere,
  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},
  interhash = {edf999afa5a0ff81e53b0c859b466659},
  intrahash = {5fbd24f07fe8784b516e69b0eb3192f3},
  journal = {Frontiers of WWW Research and Development - APWeb 2006},
  pages = {733--738},
  title = {Cubic Analysis of Social Bookmarking for Personalized Recommendation},
  url = {http://dx.doi.org/10.1007/11610113_66},
  year = 2006
}

@article{isafolksonomy2007fwn,
  author = {Noruzi, Alireza},
  interhash = {406153e7b2a8c11a963d7f14718f02d7},
  intrahash = {eaef17fef76ad3152f0300a5e9d5ddae},
  journal = {Webology},
  number = 2,
  title = {{Folksonomies: Why do we need controlled vocabulary?}},
  url = {http://www.webology.ir/2007/v4n2/editorial12.html},
  volume = 4,
  year = 2007
}

@inproceedings{hotho06trend,
  author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd},
  booktitle = {Proc. First International Conference on Semantics And Digital Media Technology (SAMT)},
  date = {2006-12-13},
  editor = {Avrithis, Yannis S. and Kompatsiaris, Yiannis and Staab, Steffen and O'Connor, Noel E.},
  ee = {http://dx.doi.org/10.1007/11930334_5},
  interhash = {227be738c5cea57530d592463fd09abd},
  intrahash = {2df7426d8ae0bd65c6f095d3fc8a703e},
  isbn = {3-540-49335-2},
  pages = {56-70},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Trend Detection in Folksonomies},
  url = {http://dblp.uni-trier.de/db/conf/samt/samt2006.html#HothoJSS06},
  vgwort = {27},
  volume = 4306,
  year = 2006
}

@incollection{schmitz2006kollaboratives,
  abstract = {Wissensmanagement in zentralisierten Wissensbasen erfordert
einen hohen Aufwand für Erstellung und Wartung, und es entspricht nicht
immer den Anforderungen der Benutzer. Wir geben in diesem Kapitel einen Überblick
über zwei aktuelle Ansätze, die durch kollaboratives Wissensmanagement
diese Probleme lösen können. Im Peer-to-Peer-Wissensmanagement unterhalten
Benutzer dezentrale Wissensbasen, die dann vernetzt werden können, um
andere Benutzer eigene Inhalte nutzen zu lassen. Folksonomies versprechen, die
Wissensakquisition so einfach wie möglich zu gestalten und so viele Benutzer in
den Aufbau und die Pflege einer gemeinsamen Wissensbasis einzubeziehen.},
  author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
  booktitle = {Semantic Web - Wege zur vernetzten Wissensgesellschaft},
  editor = {Pellegrini, Tassilo and Blumauer, Andreas},
  interhash = {cc0f3d4fa8f36968f02837e3f9f5c57b},
  intrahash = {16c101a3e00d8930832b1e8c07b31d65},
  isbn = {3-540-29324-8},
  pages = {273-290},
  publisher = {Springer},
  title = {Kollaboratives Wissensmanagement},
  url = {http://www.semantic-web.at/springer/abstracts/3d_Schmitz_KollabWM.pdf},
  vgwort = {32},
  year = 2006
}

@inproceedings{hotho2006information,
  address = {Budva, Montenegro},
  author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd},
  booktitle = {Proceedings of the 3rd European Semantic Web Conference },
  interhash = {10ec64d80b0ac085328a953bb494fb89},
  intrahash = {7da1127fc4836e2cf58e3073f1b888b2},
  isbn = {3-540-34544-2},
  month = {June},
  pages = {411-426},
  publisher = {Springer},
  series = {LNCS},
  title = {Information Retrieval in Folksonomies: Search and Ranking},
  url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2006/seach2006hotho_eswc.pdf},
  vgwort = {29},
  volume = 4011,
  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{10.1109/WI.2007.108,
  address = {Los Alamitos, CA, USA},
  author = {Nauman, Mohammad and Khan, Shahbaz},
  doi = {http://doi.ieeecomputersociety.org/10.1109/WI.2007.108},
  interhash = {ed3957694fe4ccb1137780c720b7d79a},
  intrahash = {799817443dab31b534315a790c24a9f6},
  isbn = {0-7695-3026-5},
  journal = {wi},
  pages = {423-426},
  publisher = {IEEE Computer Society},
  title = {Using PersonalizedWeb Search for Enhancing Common Sense and Folksonomy Based Intelligent Search Systems},
  volume = 0,
  year = 2007
}

@article{heylighen98bootstrapping,
  author = {Heylighen, Francis},
  comment = {meh: general ontology, knowledge structures},
  interhash = {aa8e2fb8c4118b7484d55ffc8104cd04},
  intrahash = {2fe2537d42bd46b0a31a25c125eb865d},
  journal = {Kybernetes},
  number = {5/6},
  pages = {691--722},
  title = {Bootstrapping knowledge representations: from entailment meshes via semantic nets to learning webs},
  url = {citeseer.nj.nec.com/francis96bootstrapping.html},
  volume = 30,
  year = 2001
}

@inproceedings{jaschke07recommender,
  author = {Jäschke, Robert and Marinho, Leandro Balby and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  booktitle = {Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, September 17-21, 2007, Proceedings},
  editor = {Kok, Joost N. and Koronacki, Jacek and de Mántaras, Ramon López and Matwin, Stan and Mladenic, Dunja and Skowron, Andrzej},
  ee = {http://dx.doi.org/10.1007/978-3-540-74976-9_52},
  interhash = {7e212e3bac146d406035adebff248371},
  intrahash = {b8b87c78e9e27a44aacde0402c642bff},
  isbn = {978-3-540-74975-2},
  pages = {506-514},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Tag Recommendations in Folksonomies},
  url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2007/Tag_Recommender_in_Folksonomies_final.pdf},
  vgwort = {20},
  volume = 4702,
  year = 2007
}

@inproceedings{grahl07conceptualKdml,
  author = {Grahl, Miranda and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)},
  editor = {Hinneburg, Alexander},
  interhash = {9c3bb05456bf11bcd88a1135de51f7d9},
  intrahash = {6d5188d66564fe4ed7386e28868504de},
  isbn = {978-3-86010-907-6},
  month = sep,
  pages = {50-54},
  publisher = {Martin-Luther-Universität Halle-Wittenberg},
  title = {Conceptual Clustering of Social Bookmark Sites},
  url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2007/kdml_recommender_final.pdf},
  vgwort = {14},
  year = 2007
}

@inproceedings{jaeschke07tagKdml,
  author = {Jäschke, Robert and Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd},
  booktitle = {Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)},
  editor = {Hinneburg, Alexander},
  interhash = {7e212e3bac146d406035adebff248371},
  intrahash = {bfc43dfe59f9c0935ac3364b12e6d795},
  isbn = {978-3-86010-907-6},
  month = sep,
  pages = {13-20},
  publisher = {Martin-Luther-Universität Halle-Wittenberg},
  title = {Tag Recommendations in Folksonomies},
  url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2007/kdml_recommender_final.pdf},
  vgwort = {20},
  year = 2007
}

@inproceedings{Benz07OL,
  author = {Benz, Dominik and Hotho, Andreas},
  booktitle = {LWA 2007: Lernen - Wissen - Adaption, Halle, September 2007, Workshop Proceedings (LWA)},
  crossref = {conf/lwa/2007},
  date = {2007-11-16},
  editor = {Hinneburg, Alexander},
  interhash = {ff7de5717f771dabd764675279ff3adf},
  intrahash = {ad31989b2393f5d0c4e8be8dbb613141},
  isbn = {978-3-86010-907-6},
  pages = {109-112},
  publisher = {Martin-Luther-University Halle-Wittenberg},
  title = {Position Paper: Ontology Learning from Folksonomies.},
  url = {http://dblp.uni-trier.de/db/conf/lwa/lwa2007.html#BenzH07},
  vgwort = {16},
  year = 2007
}

@inproceedings{grahl2007clustering,
  abstract = {Currently, social bookmarking systems provide intuitive support for browsing locally their content. A global view is usually presented by the tag cloud of the system, but it does not allow a conceptual drill-down, e. g., along a conceptual hierarchy. In this paper, we present a clustering approach for computing such a conceptual hierarchy for a given folksonomy. The hierarchy is complemented with ranked lists of users and resources most related to each cluster. The rankings are computed using our FolkRank algorithm. We have evaluated our approach on large scale data from the del.icio.us bookmarking system.},
  address = {Graz, Austria},
  author = {Grahl, Miranda and Hotho, Andreas and Stumme, Gerd},
  booktitle = {7th International Conference on Knowledge Management (I-KNOW '07)},
  interhash = {5cf58d2fdd3c17f0b0c54ce098ff5b60},
  intrahash = {334d3ab11400c4a3ea3ed5b1e95c1855},
  issn = {0948-695x},
  month = SEP,
  pages = {356-364},
  publisher = {Know-Center},
  title = {Conceptual Clustering of Social Bookmarking Sites},
  vgwort = {14},
  year = 2007
}

@article{wu07,
  abstract = {The subject of collective attention is central to an information age where millions of people are inundated with daily messages. It is thus of interest to understand how attention to novel items propagates and eventually fades among large populations. We have analyzed the dynamics of collective attention among 1 million users of an interactive web site, digg.com, devoted to thousands of novel news stories. The observations can be described by a dynamical model characterized by a single novelty factor. Our measurements indicate that novelty within groups decays with a stretched-exponential law, suggesting the existence of a natural time scale over which attention fades.
},
  author = {Wu, F. and Huberman, B. A.},
  doi = {10.1073/pnas.0704916104},
  eprint = {http://www.pnas.org/cgi/reprint/104/45/17599.pdf},
  interhash = {396fb48251c9919d1f5dabc2cea0ad3a},
  intrahash = {ff0a7c4758b8bfdf5cf117f652884728},
  journal = {Proc. Natl. Acad. Sci. USA},
  number = 45,
  pages = {17599-17601},
  title = {Novelty and collective attention},
  url = {http://www.pnas.org/cgi/reprint/104/45/17599.pdf},
  volume = 104,
  year = 2007
}

@inproceedings{Jaeschke2008logsonomy,
  abstract = {In social bookmarking systems users describe bookmarks
by keywords called tags. The structure behind
these social systems, called folksonomies, can be
viewed as a tripartite hypergraph of user, tag and resource
nodes. This underlying network shows specific
structural properties that explain its growth and the possibility
of serendipitous exploration.
Search engines filter the vast information of the web.
Queries describe a user’s information need. In response
to the displayed results of the search engine, users click
on the links of the result page as they expect the answer
to be of relevance. The clickdata can be represented as a
folksonomy in which queries are descriptions of clicked
URLs. This poster analyzes the topological characteristics
of the resulting tripartite hypergraph of queries,
users and bookmarks of two query logs and compares it
two a snapshot of the folksonomy del.icio.us.},
  author = {Jäschke, Robert and Krause, Beate and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008)},
  interhash = {13ec3f45fc7e0364cdc6b9a7c12c5c2c},
  intrahash = {359e1eccdc524334d4a2ad51330f76ae},
  publisher = {AAAI Press},
  title = {Logsonomy — A Search Engine Folksonomy},
  url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2008/Krause2008logsonomy_short.pdf},
  year = 2008
}

@inproceedings{xin2008www,
  abstract = {The success and popularity of social network systems, such as del.icio.us, Facebook, MySpace, and YouTube, have generated many interesting and challenging problems to the research community. Among others, discovering social interests shared by groups of users is very important because it helps to connect people with common interests and encourages people to contribute and share more contents. The main challenge to solving this problem comes from the diffi- culty of detecting and representing the interest of the users. The existing approaches are all based on the online connections of users and so unable to identify the common interest of users who have no online connections. In this paper, we propose a novel social interest discovery approach based on user-generated tags. Our approach is motivated by the key observation that in a social network, human users tend to use descriptive tags to annotate the contents that they are interested in. Our analysis on a large amount of real-world traces reveals that in general, user-generated tags are consistent with the web content they are attached to, while more concise and closer to the understanding and judgments of human users about the content. Thus, patterns of frequent co-occurrences of user tags can be used to characterize and capture topics of user interests. We have developed an Internet Social Interest Discovery system, ISID, to discover the common user interests and cluster users and their saved URLs by different interest topics. Our evaluation shows that ISID can effectively cluster similar documents by interest topics and discover user communities with common interests no matter if they have any online connections.},
  author = {Li, Xin and Guo, Lei and Zhao, Yihong E.},
  booktitle = {Proceedings of the 17th International World Wide Web Conference},
  interhash = {d7e6a5b8d215682b2a75add69c01de29},
  intrahash = {42b4c94cff05ccef031235d661a7a77a},
  pages = {675-684},
  publisher = {ACM},
  title = {Tag-based Social Interest Discovery},
  url = {http://www2008.org/papers/pdf/p675-liA.pdf},
  year = 2008
}

@article{10.1109/ITNG.2006.140,
  address = {Los Alamitos, CA, USA},
  author = {Niwa, Satoshi and Doi, Takuo and Honiden, Shinichi},
  doi = {http://doi.ieeecomputersociety.org/10.1109/ITNG.2006.140},
  interhash = {380f4bafa6f23f100225c700561e2e80},
  intrahash = {3a7b2b52257c04b0da0702a820c3f0fc},
  isbn = {0-7695-2497-4},
  journal = {itng},
  pages = {388-393},
  publisher = {IEEE Computer Society},
  title = {Web Page Recommender System based on Folksonomy Mining for ITNG ’06 Submissions},
  volume = 00,
  year = 2006
}

@article{jaeschke2008discovering,
  abstract = {Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.},
  author = {Jäschke, Robert and Hotho, Andreas and Schmitz, Christoph and Ganter, Bernhard and Stumme, Gerd},
  booktitle = {Semantic Web and Web 2.0},
  interhash = {cfca594f9dbe30694bfbcdeb40dc4e88},
  intrahash = {63901930c137df0c2dad84075c564b14},
  journal = {Web Semantics: Science, Services and Agents on the World Wide Web},
  month = feb,
  number = 1,
  pages = {38--53},
  title = {Discovering Shared Conceptualizations in Folksonomies},
  url = {http://www.sciencedirect.com/science/article/B758F-4R53WD4-1/2/ae56bd6e7132074272ca2035be13781b},
  volume = 6,
  year = 2008
}

@inbook{hotho2008bookmarking,
  address = {München},
  asin = {3486585797},
  author = {Hotho, Andreas},
  booktitle = {Web 2.0 in der Unternehmenspraxis: Grundlagen, Fallstudien und Trends zum Einsatz von Social Software},
  ean = {9783486585797},
  editor = {Back, Andrea and Gronau, Norbert and Tochtermann, Klaus},
  interhash = {1418948ca884cd3456a95b30e366ee8f},
  intrahash = {b54f6557893e3ab9d1eb83b0baeb136e},
  isbn = {9783486585797},
  pages = {26-38},
  publisher = {Oldenbourg Verlag},
  title = {Social Bookmarking},
  url = {http://www.amazon.de/gp/redirect.html%3FASIN=3486585797%26tag=ws%26lcode=xm2%26cID=2025%26ccmID=165953%26location=/Web-2-0-Unternehmenspraxis-Grundlagen-Fallstudien/dp/3486585797%253FSubscriptionId=13CT5CVB80YFWJEPWS02},
  year = 2008
}

@inproceedings{AlKhalifa:2007,
  abstract = {Users tag resources for a variety of reasons and using a variety of conventions. The tags that they provide are stored in social bookmarking services, so these services can provide a rich gateway to a wide and interesting quantity of web resources. The cognitive effort that has gone into making these tags has presumably added value to the description of the resource. In this work we utilize the quantitative value of these tags for ranking bookmarked web resources in social bookmarking services. Our proposed solution is called CoolRank, a simple and intuitive model to rank bookmarked web resources in a social bookmarking service, such as del.icio.us. CoolRank makes use of both quantitative information, based on the number of people who have bookmarked a web resource, and subjective information, based on the words people have used in their tags.},
  author = {Al-Khalifa, H.S.},
  booktitle = {Innovations in Information Technology, 2007. Innovations '07. 4th International Conference on},
  doi = {10.1109/IIT.2007.4430482},
  interhash = {a6babb1a2f926cca3e8fe0258337e864},
  intrahash = {4671fb1c606e3d7f559bb25d9b20e47d},
  isbn = {978-1-4244-1841-1},
  pages = {208-212},
  title = {CoolRank: A Social Solution for Ranking Bookmarked Web Resources},
  url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4430482},
  year = 2007
}

@inproceedings{Detecting_Commmunities_via_Simultaneous_Clustering_of_Graphs_and_Folksonomies,
  author = {Java, Akshay and Joshi, Anupam and Finin, Tim},
  booktitle = {WebKDD 2008 Workshop on Web Mining and Web Usage Analysis},
  interhash = {acfec953843b168e61e2e167e29b4c3d},
  intrahash = {645abd6b3191a2a6e844d7542651ed1c},
  month = {August},
  note = {To Appear},
  title = {{Detecting Commmunities via Simultaneous Clustering of Graphs and Folksonomies}},
  year = 2008
}

@article{1377474,
  abstract = {Many Web sites have begun allowing users to submit items to a collection and tag them with keywords. The folksonomies built from these tags are an interesting topic that has seen little empirical research. This study compared the search information retrieval (IR) performance of folksonomies from social bookmarking Web sites against search engines and subject directories. Thirty-four participants created 103 queries for various information needs. Results from each IR system were collected and participants judged relevance. Folksonomy search results overlapped with those from the other systems, and documents found by both search engines and folksonomies were significantly more likely to be judged relevant than those returned by any single IR system type. The search engines in the study had the highest precision and recall, but the folksonomies fared surprisingly well. Del.icio.us was statistically indistinguishable from the directories in many cases. Overall the directories were more precise than the folksonomies but they had similar recall scores. Better query handling may enhance folksonomy IR performance further. The folksonomies studied were promising, and may be able to improve Web search performance.},
  address = {Tarrytown, NY, USA},
  author = {Morrison, P. Jason},
  doi = {http://dx.doi.org/10.1016/j.ipm.2007.12.010},
  interhash = {41f042c033417e4dbb9e48b76521363f},
  intrahash = {7e1dc3f52085093cc33d8fe931253b34},
  issn = {0306-4573},
  journal = {Inf. Process. Manage.},
  number = 4,
  pages = {1562--1579},
  publisher = {Pergamon Press, Inc.},
  title = {Tagging and searching: Search retrieval effectiveness of folksonomies on the World Wide Web},
  url = {http://portal.acm.org/citation.cfm?id=1377474},
  volume = 44,
  year = 2008
}

@article{ciro2006semiotic,
  abstract = {Abstract  A distributed classification paradigm known as collaborative tagging has been successfully deployed in large-scale web applications designed to manage and share diverse online resources. Users of these applications organize resources by associating with them freely chosen text labels, or tags. Here we regard tags as basic dynamical entities and study the semiotic dynamics underlying collaborative tagging. We collect data from a popular system and focus on tags associated with a given resource.We find that the frequencies of tags obey to a generalized Zipf’s law and show that a Yule–Simon process with memory can beused to explain the observed frequency distributions in terms of a simple model of user behavior},
  author = {Cattuto, Ciro},
  interhash = {6651fe8b8916e8407f738325c092b860},
  intrahash = {86a43b0d0b4956b3ff6b553f78277ec9},
  journal = {The European Physical Journal C - Particles and Fields},
  month = {#aug#},
  number = 0,
  pages = {33--37},
  title = {Semiotic dynamics in online social communities},
  url = {http://dx.doi.org/10.1140/epjcd/s2006-03-004-4},
  volume = 46,
  year = 2006
}

@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{1454053,
  address = {New York, NY, USA},
  author = {Bogers, Toine and van den Bosch, Antal},
  booktitle = {RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems},
  doi = {http://doi.acm.org/10.1145/1454008.1454053},
  interhash = {692eb1215676da39997ad861b681c450},
  intrahash = {9d0d8ca850db6cf6177efc66e16785b7},
  isbn = {978-1-60558-093-7},
  location = {Lausanne, Switzerland},
  pages = {287--290},
  publisher = {ACM},
  title = {Recommending scientific articles using citeulike},
  url = {http://portal.acm.org/citation.cfm?id=1454053},
  year = 2008
}

@article{4686305,
  abstract = {Social tagging systems have recently became very popular as a means to classify large sets of resources shared among on-line communities over the social Web. However, the folksonomies resulting from the use of these systems revealed limitations : tags are ambiguous and their spelling may vary, and folksonomies are difficult to exploit in order to retrieve or exchange information. This article compares the recent attempts to overcome these limitations and to support the use of folksonomies with formal languages and ontologies from the Semantic Web.},
  author = {Limpens, Freddy and Gandon, Fabien and Buffa, Michel},
  doi = {10.1109/ASEW.2008.4686305},
  interhash = {cb1d534be80d664a50df66e8977b774e},
  intrahash = {9372f9c2db8b9f4cf05b3db84e6589ac},
  journal = {Automated Software Engineering - Workshops, 2008. ASE Workshops 2008. 23rd IEEE/ACM International Conference on},
  month = {Sept.},
  pages = {13-18},
  title = {Bridging ontologies and folksonomies to leverage knowledge sharing on the social Web: A brief survey},
  year = 2008
}

@article{1751-8121-41-22-224016,
  abstract = {We analyze CiteULike, an online collaborative tagging system where users bookmark and annotate scientific papers. Such a system can be naturally represented as a tri-partite graph whose nodes represent papers, users and tags connected by individual tag assignments. The semantics of tags is studied here, in order to uncover the hidden relationships between tags. We find that the clustering coefficient can be used to analyze the semantical patterns among tags.},
  author = {Capocci, Andrea and Caldarelli, Guido},
  interhash = {14c8ecbc38dcdae876c3f4401006e3bb},
  intrahash = {2a219a2664c566b405420f720583643a},
  journal = {Journal of Physics A: Mathematical and Theoretical},
  number = 22,
  pages = {224016 (7pp)},
  title = {Folksonomies and clustering in the collaborative system CiteULike},
  url = {http://stacks.iop.org/1751-8121/41/224016},
  volume = 41,
  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{1502661,
  abstract = {While recommender systems tell users what items they might like, explanations of recommendations reveal why they might like them. Explanations provide many benefits, from improving user satisfaction to helping users make better decisions. This paper introduces tagsplanations, which are explanations based on community tags. Tagsplanations have two key components: tag relevance, the degree to which a tag describes an item, and tag preference, the user's sentiment toward a tag. We develop novel algorithms for estimating tag relevance and tag preference, and we conduct a user study exploring the roles of tag relevance and tag preference in promoting effective tagsplanations. We also examine which types of tags are most useful for tagsplanations.},
  address = {New York, NY, USA},
  author = {Vig, Jesse and Sen, Shilad and Riedl, John},
  booktitle = {IUI '09: Proceedingsc of the 13th international conference on Intelligent user interfaces},
  doi = {http://doi.acm.org/10.1145/1502650.1502661},
  interhash = {a6d866cf13c75130c1969c9e40606fd1},
  intrahash = {1e74fa227a24f49d8f6b17a02ea96db5},
  isbn = {978-1-60558-168-2},
  location = {Sanibel Island, Florida, USA},
  pages = {47--56},
  publisher = {ACM},
  title = {Tagsplanations: explaining recommendations using tags},
  url = {http://portal.acm.org/citation.cfm?id=1502650.1502661},
  year = 2008
}

@mastersthesis{stützer2008ol,
  address = {Kassel},
  author = {Stützer, Stefan},
  interhash = {9426b67db29c7270955ae22202c28c82},
  intrahash = {23b133bc2e6a4e00ab243efa98a02a12},
  school = {University of Kassel},
  title = {Lernen von Ontologien aus kollaborativen Tagging-Systemen},
  type = {Master Thesis},
  year = 2009
}

@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
}

@inproceedings{Angeletou08semanticallyenriching,
  abstract = {Abstract. While the increasing popularity of folksonomies has lead to a vast quantity of tagged data, resource retrieval in folksonomies is limited by being agnostic to the meaning (i.e., semantics) of tags. Our goal is to automatically enrich folksonomy tags (and implicitly the related resources) with formal semantics by associating them to relevant concepts defined in online ontologies. We introduce FLOR, a method that performs automatic folksonomy enrichment by combining knowledge from WordNet and online available ontologies. Experimentally testing FLOR, we found that it correctly enriched 72 % of 250 Flickr photos. 1},
  author = {Angeletou, Sofia and Sabou, Marta and Motta, Enrico},
  booktitle = {In Proc of the 5th ESWC. workshop: Collective Intelligence & the Semantic Web},
  interhash = {1b244d0220730e994822192f6e1cba76},
  intrahash = {cd78f2e97127932ea36b7014c3d15aa6},
  title = {Semantically enriching folksonomies with FLOR},
  url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.2569},
  year = 2008
}

@inproceedings{1379123,
  abstract = {Social bookmarking systems constitute an established part of the Web 2.0. In such systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration.
Today's search engines represent the gateway to retrieve information from the World Wide Web. Short queries typically consisting of two to three words describe a user's information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance.
This clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonomies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a large snapshot of del.icio.us and on query logs of two large search engines. All of the three datasets show small world properties. The tagging behavior of users, which is explained by preferential attachment of the tags in social bookmark systems, is reflected in the distribution of single query words in search engines. We can conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of social bookmarking users is driven by similar dynamics.},
  address = {New York, NY, USA},
  author = {Krause, Beate and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd},
  booktitle = {HT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia},
  doi = {http://doi.acm.org/10.1145/1379092.1379123},
  interhash = {6d34ea1823d95b9dbf37d4db4d125d2a},
  intrahash = {c7f43f2f922de1e7febedd10347e80cb},
  isbn = {978-1-59593-985-2},
  location = {Pittsburgh, PA, USA},
  pages = {157--166},
  publisher = {ACM},
  title = {Logsonomy - social information retrieval with logdata},
  url = {http://portal.acm.org/citation.cfm?id=1379092.1379123&coll=ACM&dl=ACM&type=series&idx=SERIES399&part=series&WantType=Proceedings&title=HT&CFID=825963&CFTOKEN=78379687},
  year = 2008
}

@article{journals/www/EdaYUU09,
  author = {Eda, Takeharu and Yoshikawa, Masatoshi and Uchiyama, Toshio and Uchiyama, Tadasu},
  ee = {http://dx.doi.org/10.1007/s11280-009-0069-1},
  interhash = {a560796c977bc7582017f662bf88c16d},
  intrahash = {ec3c256e7d1f24cd9d407d3ce7e41d96},
  journal = {World Wide Web},
  number = 4,
  pages = {421-440},
  title = {The Effectiveness of Latent Semantic Analysis for Building Up a Bottom-up Taxonomy from Folksonomy Tags.},
  url = {http://dblp.uni-trier.de/db/journals/www/www12.html#EdaYUU09},
  volume = 12,
  year = 2009
}

@incollection{reference/rsh/MarinhoNSJHSS11,
  author = {Marinho, Leandro Balby and Nanopoulos, Alexandros and Schmidt-Thieme, Lars and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd and Symeonidis, Panagiotis},
  booktitle = {Recommender Systems Handbook},
  crossref = {reference/rsh/2011},
  editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha and Kantor, Paul B.},
  ee = {http://dx.doi.org/10.1007/978-0-387-85820-3_19},
  interhash = {2d4afa6f7fb103ccc166c9c5d629cdd1},
  intrahash = {8a520671b6ced7c4b81b1cd18274e0ee},
  isbn = {978-0-387-85819-7},
  pages = {615-644},
  publisher = {Springer},
  title = {Social Tagging Recommender Systems.},
  url = {http://dblp.uni-trier.de/db/reference/rsh/rsh2011.html#MarinhoNSJHSS11},
  year = 2011
}

@inproceedings{cattuto2008semantic,
  abstract = {Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For tasks like synonym detection and discovery of concept hierarchies, many researchers introduced measures of tag similarity. Eventhough most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptionson the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity interms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures oftag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding isprovided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measuresof semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of theinvestigated similarity measures and indicates which ones are better suited in the context of a given semantic application.},
  author = {Cattuto, Ciro and Benz, Dominik and Hotho, Andreas and Stumme, Gerd},
  booktitle = {The Semantic Web - ISWC 2008},
  doi = {10.1007/978-3-540-88564-1_39},
  interhash = {b44538648cfd476d6c94e30bc6626c86},
  intrahash = {4752f261d03cead0c52565148a0ba1c9},
  isbn = {978-3-540-88563-4},
  pages = {615--631},
  publisher = {Springer Berlin / Heidelberg},
  series = {Lecture Notes in Computer Science},
  title = {Semantic Grounding of Tag Relatedness in Social Bookmarking Systems},
  url = {http://www.kde.cs.uni-kassel.de/pub/pdf/cattuto2008semantica.pdf},
  volume = 5318,
  year = 2008
}

@inproceedings{Kim2008,
  address = {Berlin, Deutschland},
  author = {Kim, Hak Lae and Scerri, Simon and Breslin, John G. and Decker, Stefan and Kim, Hong Gee},
  booktitle = {{Proceedings of the 2008 International Conference on Dublin Core and Metadata Applications}},
  interhash = {9c5f5af6f47a1a563dbb405c5a58a3cc},
  intrahash = {7d3c3c2189394a8686ca9812d58bfe74},
  pages = {128--137},
  publisher = {{Dublin Core Metadata Initiative}},
  title = {{The State of the Art in Tag Ontologies: A Semantic Model for Tagging and Folksonomies}},
  year = 2008
}

@inproceedings{rezel2010swefe,
  abstract = {This paper presents SWE-FE: a suite of methods to extend folksonomies to the worldwide Sensor Web in order to tackle the emergent data rich information poor (DRIP) syndrome afflicting most geospatial applications on the Internet. SWE-FE leverages the geospatial information associated with three key components of such collaborative tagging systems: tags, resources and users. Specifically, SWE-FE provides algorithms for: i) suggesting tags for users during the tag input stage; ii) generating tag maps which provides for serendipitous browsing; and iii) personalized searching within the folksonomy. We implement SWE-FE on the GeoCENS Sensor Web platform as a case study for assessing the efficacy of our methods. We outline the evaluation framework that we are currently employing to carry out this assessment.},
  author = {Rezel, R. and Liang, S.},
  booktitle = {2010 International Symposium on Collaborative Technologies and Systems (CTS)},
  doi = {10.1109/CTS.2010.5478494},
  interhash = {9eb696593932c517873232386f8f61bf},
  intrahash = {d5b71572c7fea6504a0c0a3d84a9ecf0},
  month = may,
  pages = {349--356},
  publisher = {IEEE},
  title = {SWE-FE: Extending folksonomies to the Sensor Web},
  url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5478494},
  year = 2010
}

@phdthesis{bogers2009recommender,
  abstract = {Recommender systems belong to a class of personalized information filtering technologies that aim to identify which items in a collection might be of interest to a particular user. Recommendations can be made using a variety of information sources related to both the user and the items: past user preferences, demographic information, item popularity, the metadata characteristics of the products, etc. Social bookmarking websites, with their emphasis on open collaborative information access, offer an ideal scenario for the application of recommender systems technology. They allow users to manage their favorite bookmarks online through a web interface and, in many cases, allow their users to tag the content they have added to the system with keywords. The underlying application then makes all information sharable among users. Examples of social bookmarking services include Delicious, Diigo, Furl, CiteULike, and BibSonomy. In my Ph.D. thesis I describe the work I have done on item recommendation for social bookmarking, i.e., recommending interesting bookmarks to users based on the content they bookmarked in the past. In my experiments I distinguish between two types of information sources. The first one is usage data contained in the folksonomy, which represents the past selections and transactions of all users, i.e., who added which items, and with what tags. The second information source is the metadata describing the bookmarks or articles on a social bookmarking website, such as title, description, authorship, tags, and temporal and publication-related metadata. I compare and combine the content-based aspect with the more common usage-based approaches. I evaluate my approaches on four data sets constructed from three different social bookmarking websites: BibSonomy, CiteULike, and Delicious. In addition, I investigate different combination methods for combining different algorithms and show which of those methods can successfully improve recommendation performance. Finally, I consider two growing pains that accompany the maturation of social bookmarking websites: spam and duplicate content. I examine how widespread each of these problems are for social bookmarking and how to develop effective automatic methods for detecting such unwanted content. Finally, I investigate the influence spam and duplicate content can have on item recommendation. },
  address = {Tilburg, The Netherlands},
  author = {Bogers, Toine},
  interhash = {65b74dcabaa583a48469f3dec2ec1f62},
  intrahash = {b02daac1201473600b7c8d2553865b4a},
  month = dec,
  school = {Tilburg University},
  title = {Recommender Systems for Social Bookmarking},
  url = {http://ilk.uvt.nl/~toine/phd-thesis/},
  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 = {23},
  volume = 6581,
  year = 2011
}

@incollection{jaeschke2012challenges,
  abstract = {Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.},
  address = {Berlin/Heidelberg},
  affiliation = {Knowledge & Data Engineering Group, University of Kassel, Wilhelmshöher Allee 73, 34121 Kassel, Germany},
  author = {Jäschke, Robert and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd},
  booktitle = {Recommender Systems for the Social Web},
  doi = {10.1007/978-3-642-25694-3_3},
  editor = {Pazos Arias, José J. and Fernández Vilas, Ana and Díaz Redondo, Rebeca P.},
  interhash = {75b1a6f54ef54d0126d0616b5bf77563},
  intrahash = {7d41d332cccc3e7ba8e7dadfb7996337},
  isbn = {978-3-642-25694-3},
  pages = {65--87},
  publisher = {Springer},
  series = {Intelligent Systems Reference Library},
  title = {Challenges in Tag Recommendations for Collaborative Tagging Systems},
  url = {http://dx.doi.org/10.1007/978-3-642-25694-3_3},
  volume = 32,
  year = 2012
}

@book{balbymarinho2012recommender,
  abstract = {Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.},
  author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.},
  interhash = {0bb7f0588cd690d67cc73e219a3a24fa},
  intrahash = {87d6883ebd98e8810be45d7e7e4ade96},
  isbn = {978-1-4614-1893-1},
  month = feb,
  publisher = {Springer},
  series = {SpringerBriefs in Electrical and Computer Engineering},
  title = {Recommender Systems for Social Tagging Systems},
  url = {http://www.springer.com/computer/database+management+%26+information+retrieval/book/978-1-4614-1893-1},
  year = 2012
}

@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
}

@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.</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 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{Laniado2010,
  author = {Laniado, David and Mika, Peter},
  booktitle = {International Semantic Web Conference (1)},
  crossref = {conf/semweb/2010-1},
  editor = {Patel-Schneider, Peter F. and Pan, Yue and Hitzler, Pascal and Mika, Peter and Zhang, Lei and Pan, Jeff Z. and Horrocks, Ian and Glimm, Birte},
  ee = {http://dx.doi.org/10.1007/978-3-642-17746-0_30},
  interhash = {3a63f88e11f958d548fa91fe442e1dcf},
  intrahash = {58dace4881efbd12c81ef1cc2e6bf7b9},
  isbn = {978-3-642-17745-3},
  pages = {470-485},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Making Sense of Twitter.},
  url = {http://dblp.uni-trier.de/db/conf/semweb/iswc2010-1.html#LaniadoM10},
  volume = 6496,
  year = 2010
}

@article{Zhang20125759,
  abstract = {Social tagging is one of the most important ways to organize and index online resources. Recommendation in social tagging systems, e.g. tag recommendation, item recommendation and user recommendation, is used to improve the quality of tags and to ease the tagging or searching process. Existing works usually provide recommendations by analyzing relation information in social tagging systems, suffering a lot from the over sparse problem. These approaches ignore information contained in the content of resources, which we believe should be considered to improve recommendation quality and to deal with the over sparse problem. In this paper we propose a recommendation approach for social tagging systems that combines content and relation analysis in a single model. By modeling the generating process of social tagging systems in a latent Dirichlet allocation approach, we build a fully generative model for social tagging, leverage it to estimate the relation between users, tags and resources and achieve tag, item and user recommendation tasks. The model is evaluated using a CiteULike data snapshot, and results show improvements in metrics for various recommendation tasks.},
  author = {Zhang, Yin and Zhang, Bin and Gao, Kening and Guo, Pengwei and Sun, Daming},
  doi = {10.1016/j.physa.2012.05.013},
  interhash = {088ad59c786579d399aaee48db5e6a7a},
  intrahash = {84f824839090a5e20394b85a9e1cef08},
  issn = {0378-4371},
  journal = {Physica A: Statistical Mechanics and its Applications},
  number = 22,
  pages = {5759 - 5768},
  title = {Combining content and relation analysis for recommendation in social tagging systems},
  url = {http://www.sciencedirect.com/science/article/pii/S0378437112003846},
  volume = 391,
  year = 2012
}

@article{10.1109/TKDE.2012.115,
  address = {Los Alamitos, CA, USA},
  author = {Zubiaga, Arkaitz and Fresno, Victor and Martinez, Raquel and Garcia-Plaza, Alberto P.},
  doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2012.115},
  interhash = {f2e961e2b99fec0634b0d4fa3e001282},
  intrahash = {8a25332bfeb33e2ad8e1e1a062976da2},
  issn = {1041-4347},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  number = {PrePrints},
  publisher = {IEEE Computer Society},
  title = {Harnessing Folksonomies to Produce a Social Classification of Resources},
  volume = 99,
  year = 2012
}

@article{landia2013deeper,
  abstract = {The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.},
  author = {Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan},
  interhash = {e8095b13630452ce3ecbae582f32f4bc},
  intrahash = {e585a92994be476480545eb62d741642},
  journal = {cs.IR},
  title = {Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations},
  url = {http://arxiv.org/abs/1310.1498},
  volume = {1310.1498},
  year = 2013
}

@inproceedings{mueller2013recommendations,
  abstract = {With the rising popularity of smart mobile devices, sensor data-based
 applications have become more and more popular. Their users record
 data during their daily routine or specifically for certain events.
 The application WideNoise Plus allows users to record sound samples
 and to annotate them with perceptions and tags. The app is being
 used to document and map the soundscape all over the world. The procedure
 of recording, including the assignment of tags, has to be as easy-to-use
 as possible. We therefore discuss the application of tag recommender
 algorithms in this particular scenario. We show, that this task is
 fundamentally different from the well-known tag recommendation problem
 in folksonomies as users do no longer tag fix resources but rather
 sensory data and impressions. The scenario requires efficient recommender
 algorithms that are able to run on the mobile device, since Internet
 connectivity cannot be assumed to be available. Therefore, we evaluate
 the performance of several tag recommendation algorithms and discuss
 their applicability in the mobile sensing use-case.},
  address = {Aachen, Germany},
  author = {Mueller, Juergen and Doerfel, Stephan and Becker, Martin and Hotho, Andreas and Stumme, Gerd},
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  interhash = {23d1cf49208d9a0c8b883dc69d4e444d},
  intrahash = {2bab3f013052bc741e795c5c61aea5c9},
  issn = {1613-0073},
  publisher = {CEUR-WS},
  title = {Tag Recommendations for SensorFolkSonomies},
  url = {http://ceur-ws.org/Vol-1066/},
  volume = 1066,
  year = 2013
}

@article{cimiano05learning,
  author = {Cimiano, Philipp and Hotho, Andreas and Staab, Steffen},
  ee = {http://www.jair.org/papers/paper1648.html},
  interhash = {4c09568cff62babd362aab03095f4589},
  intrahash = {eaaf0e4b3a8b29fab23b6c15ce2d308d},
  journal = {Journal on Artificial Intelligence Research},
  pages = {305-339},
  title = {Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis},
  url = {http://dblp.uni-trier.de/db/journals/jair/jair24.html#CimianoHS05},
  volume = 24,
  year = 2005
}

@incollection{singer2014folksonomies,
  author = {Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus},
  booktitle = {Encyclopedia of Social Network Analysis and Mining},
  interhash = {3a55606e91328ca0191127b1fafe189e},
  intrahash = {84d9498b73de976d8d550c6761d4be0d},
  pages = {542--547},
  publisher = {Springer},
  title = {Folksonomies},
  year = 2014
}