@article{jaeschke2008tag,
  abstract = {Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied.
In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of
user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences.  We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.
},
  address = {Amsterdam},
  author = {Jäschke, Robert and Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd},
  doi = {10.3233/AIC-2008-0438},
  editor = {Giunchiglia, Enrico},
  interhash = {b2f1aba6829affc85d852ea93a8e39f7},
  intrahash = {955bcf14f3272ba6eaf3dadbef6c0b10},
  issn = {0921-7126},
  journal = {AI Communications},
  number = 4,
  pages = {231-247},
  publisher = {IOS Press},
  title = {Tag Recommendations in Social Bookmarking Systems},
  url = {http://dx.doi.org/10.3233/AIC-2008-0438},
  vgwort = {63},
  volume = 21,
  year = 2008
}

@inproceedings{krause2008comparison,
  abstract = {Social bookmarking systems allow users to store links to internet resources on a web page. As social bookmarking systems are growing in popularity, search algorithms have been developed that transfer the idea of link-based rankings in the Web to a social bookmarking system’s
data structure. These rankings differ from traditional search engine rankings in that they incorporate the rating of users. 

In this study, we compare search in social bookmarking systems with traditionalWeb search. In the first part, we compare the user activity and behaviour in both kinds of systems, as well as the overlap of the underlying sets of URLs. In the second part,we compare graph-based and vector space rankings for social bookmarking systems with commercial search engine rankings.

Our experiments are performed on data of the social bookmarking system Del.icio.us and on rankings and log data from Google, MSN, and AOL. We will show that part of the difference between the systems is due to different behaviour (e. g., the concatenation of multi-word lexems
to single terms in Del.icio.us), and that real-world events may trigger similar behaviour in both kinds of systems. We will also show that a graph-based ranking approach on folksonomies yields results that are closer to the rankings of the commercial search engines than vector space
retrieval, and that the correlation is high in particular for the domains that are well covered by the social bookmarking system.},
  address = {Heidelberg},
  author = {Krause, Beate and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Advances in Information Retrieval, 30th European Conference on IR Research, ECIR 2008},
  editor = {Macdonald, Craig and Ounis, Iadh and Plachouras, Vassilis and Ruthven, Ian and White, Ryen W.},
  interhash = {37598733b747093d97a0840a11beebf5},
  intrahash = {86dbae2e531c7f68592f5780b46b6a70},
  pages = {101-113},
  publisher = {Springer},
  series = {LNAI},
  title = {A Comparison of Social Bookmarking  with Traditional Search},
  volume = 4956,
  year = 2008
}

@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{benz2009characterizing,
  address = {Bled, Slovenia},
  author = {Benz, Dominik and Krause, Beate and Kumar, G. Praveen and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Proceedings of the 1st Workshop on Explorative Analytics of Information Networks (EIN2009)},
  interhash = {de5e58b26200e44112d9791f39e7523d},
  intrahash = {b697a98a7340585594455ee2e81d238a},
  month = {September},
  title = {Characterizing Semantic Relatedness of Search Query Terms},
  year = 2009
}

@inproceedings{conf/wsdm/AntonellisGK09,
  author = {Antonellis, Ioannis and Garcia-Molina, Hector and Karim, Jawed},
  booktitle = {WSDM (Late Breaking-Results)},
  crossref = {conf/wsdm/2009},
  date = {2009-03-12},
  editor = {Baeza-Yates, Ricardo A. and Boldi, Paolo and Ribeiro-Neto, Berthier A. and Cambazoglu, Berkant Barla},
  ee = {http://www.wsdm2009.org/wsdm2009_antonellis.pdf},
  interhash = {d7009d789ebc4efe38749a1078a06086},
  intrahash = {70539954a20f7d03a1f21764ff62c0ff},
  isbn = {978-1-60558-390-7},
  publisher = {ACM},
  title = {Tagging with Queries: How and Why?},
  url = {http://dblp.uni-trier.de/db/conf/wsdm/wsdm2009.html#AntonellisGK09},
  year = 2009
}