  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
NameRank 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.
