@misc{mitzlaff2013recommending,
  abstract = {All over the world, future parents are facing the task of finding a suitable
given name for their child. This choice is influenced by different factors,
such as the social context, language, cultural background and especially
personal taste. Although this task is omnipresent, little research has been
conducted on the analysis and application of interrelations among given names
from a data mining perspective.
  The present work tackles the problem of recommending given names, by firstly
mining for inter-name relatedness in data from the Social Web. Based on these
results, the name search engine "Nameling" was built, which attracted more than
35,000 users within less than six months, underpinning the relevance of the
underlying recommendation task. The accruing usage data is then used for
evaluating different state-of-the-art recommendation systems, as well our new
\NR algorithm which we adopted from our previous work on folksonomies and which
yields the best results, considering the trade-off between prediction accuracy
and runtime performance as well as its ability to generate personalized
recommendations. We also show, how the gathered inter-name relationships can be
used for meaningful result diversification of PageRank-based recommendation
systems.
  As all of the considered usage data is made publicly available, the present
work establishes baseline results, encouraging other researchers to implement
advanced recommendation systems for given names.},
  author = {Mitzlaff, Folke and Stumme, Gerd},
  interhash = {545658b6e337858f7865b51e46d1c7a6},
  intrahash = {41f92650f0f7d78366febc1832cedba9},
  note = {cite arxiv:1302.4412Comment: Baseline results for the ECML PKDD Discovery Challenge 2013},
  title = {Recommending Given Names},
  url = {http://arxiv.org/abs/1302.4412},
  year = 2013
}

@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 = {Kacprzyk, Janusz and Jain, Lakhmi C.},
  interhash = {75b1a6f54ef54d0126d0616b5bf77563},
  intrahash = {b645572f4c635b2d674126f26d4303d6},
  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
}